We live in a time when video games are extremely popular. The global video game market continues to grow year-on-year, and the industry is now valued at over $100 billion worldwide. With technology continuously pushing the boundaries, video games have only become more popular and more high-quality. Gameplay mechanics, cutting-edge graphics, and intricate storylines make today’s games more immersive than ever before. We chose this dataset to gain insights on the popularity of upcoming games.
Popular’ is our class label, we will use Global_Sales attribute to predict whether a game will sell 1000000 or more globally.
Our data mining task is to predict the popularity of upcoming games using regression.
The dataset provided by vgchartz.com supply us with a valuable resource to explore the platforms and genres of the top 16599 global video games. Through it, we can analyze the most popular platforms and genres that are influencing global sales, and detectr how regions’ sales affect global sales.
Our goal from studying this dataset is to utilize regression techniques on the input data to make predictions about the popularity of upcoming games.
| Attributes name | Description | Data type |
|---|---|---|
| Rank | Ranking of the game based on global sales. | Numeric |
| Name | Name of the game. | Nominal |
| Platform | Platform the game was released on. | Nominal |
| Year | Year the game was released. | Ordinal |
| Genre | Genre of the game | Nominal |
| Publisher | Publisher of the game. | Nominal |
| NA_Sales | Sales of the game in North America | Numeric (ratio-scaled) |
| EU_Sales | Sales of the game in Europe | Numeric (ratio-scaled) |
| JP_Sales | Sales of the game in Japan | Numeric (ratio-scaled) |
| Other_Sales | Sales of the game in other regions | Numeric (ratio-scaled) |
| Global_Sales | Total sales of the game worldwide | Numeric (ratio-scaled) |
library(outliers)
library(dplyr)
library(Hmisc)
library(ggplot2)
library(mlbench)
library(caret)
library(factoextra)
library(cluster)
=======
library(outliers)
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
library(Hmisc)
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Attaching package: ‘Hmisc’
The following objects are masked from ‘package:dplyr’:
src, summarize
The following objects are masked from ‘package:base’:
format.pval, units
library(ggplot2)
library(mlbench)
library(caret)
Loading required package: lattice
library(factoextra)
Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(cluster)
>>>>>>> Stashed changes
options(max.print=9999999)
dataset=read.csv("Dataset/vgsales.csv")
checking number of rows and columns, and cheking dimensionality and coulumns names:
<<<<<<< Updated upstreamnrow(dataset)
ncol(dataset)
dim(dataset)
names(dataset)
=======
nrow(dataset)
[1] 16598
ncol(dataset)
[1] 11
dim(dataset)
[1] 16598 11
names(dataset)
[1] "Rank" "Name" "Platform" "Year" "Genre" "Publisher" "NA_Sales" "EU_Sales" "JP_Sales"
[10] "Other_Sales" "Global_Sales"
>>>>>>> Stashed changes
Dataset structure:
str(dataset)
<<<<<<< Updated upstream
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'data.frame': 16598 obs. of 11 variables:
$ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Name : chr "Wii Sports" "Super Mario Bros." "Mario Kart Wii" "Wii Sports Resort" ...
$ Platform : chr "Wii" "NES" "Wii" "Wii" ...
$ Year : chr "2006" "1985" "2008" "2009" ...
$ Genre : chr "Sports" "Platform" "Racing" "Sports" ...
$ Publisher : chr "Nintendo" "Nintendo" "Nintendo" "Nintendo" ...
$ NA_Sales : num 41.5 29.1 15.8 15.8 11.3 ...
$ EU_Sales : num 29.02 3.58 12.88 11.01 8.89 ...
$ JP_Sales : num 3.77 6.81 3.79 3.28 10.22 ...
$ Other_Sales : num 8.46 0.77 3.31 2.96 1 0.58 2.9 2.85 2.26 0.47 ...
$ Global_Sales: num 82.7 40.2 35.8 33 31.4 ...
>>>>>>> Stashed changes
sample of raw dataset(first 10 rows):
head(dataset, 10)
<<<<<<< Updated upstream
=======
sample of raw dataset(last 10 rows):
tail(dataset, 10)
<<<<<<< Updated upstream
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Five number summary of each attribute in our dataset:
summary(dataset)
<<<<<<< Updated upstream
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Rank Name Platform Year Genre Publisher NA_Sales EU_Sales
Min. : 1 Length:16598 Length:16598 Length:16598 Length:16598 Length:16598 Min. : 0.0000 Min. : 0.0000
1st Qu.: 4151 Class :character Class :character Class :character Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.0000
Median : 8300 Mode :character Mode :character Mode :character Mode :character Mode :character Median : 0.0800 Median : 0.0200
Mean : 8301 Mean : 0.2647 Mean : 0.1467
3rd Qu.:12450 3rd Qu.: 0.2400 3rd Qu.: 0.1100
Max. :16600 Max. :41.4900 Max. :29.0200
JP_Sales Other_Sales Global_Sales
Min. : 0.00000 Min. : 0.00000 Min. : 0.0100
1st Qu.: 0.00000 1st Qu.: 0.00000 1st Qu.: 0.0600
Median : 0.00000 Median : 0.01000 Median : 0.1700
Mean : 0.07778 Mean : 0.04806 Mean : 0.5374
3rd Qu.: 0.04000 3rd Qu.: 0.04000 3rd Qu.: 0.4700
Max. :10.22000 Max. :10.57000 Max. :82.7400
>>>>>>> Stashed changes
variance of numeric data:
<<<<<<< Updated upstreamvar(dataset$NA_Sales)
var(dataset$EU_Sales)
var(dataset$JP_Sales)
var(dataset$Other_Sales)
var(dataset$Global_Sales)
=======
var(dataset$NA_Sales)
[1] 0.6669712
var(dataset$EU_Sales)
[1] 0.2553799
var(dataset$JP_Sales)
[1] 0.0956607
var(dataset$Other_Sales)
[1] 0.03556559
var(dataset$Global_Sales)
[1] 2.418112
>>>>>>> Stashed changes
dataset2 <- dataset %>% sample_n(50)
tab <- dataset2$Platform %>% table()
precentages <- tab %>% prop.table() %>% round(3) * 100
txt <- paste0(names(tab), '\n', precentages, '%')
pie(tab, labels=txt , main = "Pie chart of Platform")
<<<<<<< Updated upstream
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This pie chart illustrate platforms of global video games , We notice from the pie chart of platform attribute that releasing a game for PS users will increase the popularity of the game since it is the most common platform among gamers.
# coloring barplot and adding text
tab<-dataset$Genre %>% table()
precentages<-tab %>% prop.table() %>% round(3)*100
txt<-paste0(names(tab), '\n',precentages,'%')
bb <- dataset$Genre %>% table() %>% barplot(axisnames=F, main = "Barplot for Popular genres ",ylab='count',col=c('pink','blue','lightblue','green','lightgreen','red','orange','red','grey','yellow','azure','olivedrab'))
text(bb,tab/2,labels=txt,cex=1.5)
<<<<<<< Updated upstream
=======
This barplot illustrates popularity of global video games genres ,In terms of genre, action games are the most popular, followed by sports and music games. It is safe to assume that a high number of genres of this nature exist due to their popularity and sales.
boxplot(dataset$NA_Sales , main="
BoxPlot for NA_Sales")
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The boxplot of the NA_Sales (Sales of the game in north America) attribute indicates that the values are close to each other ,and there are a lot of outliers since the dataset represents all the north America sales of video games.
boxplot(dataset$EU_Sales, main="
BoxPlot for EU_Sales")
<<<<<<< Updated upstream
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The boxplot of the EU_Sales (sales of the game in Europe) attribute indicates that the values are close to each other, and there are a lot of outliers since the dataset represents all the Europe sales of video games.
boxplot(dataset$JP_Sales , main="
BoxPlot for JP_Sales")
<<<<<<< Updated upstream
=======
The boxplot of the JP_Sales (sales of the game in Japan) attribute indicates that the values are close to each other, and there are a lot of outliers since the dataset represents all the Japan sales of video games.
boxplot(dataset$Other_Sales , main="
BoxPlot for Other_Sales")
<<<<<<< Updated upstream
=======
The boxplot of the Other-sales attribute indicate that the values are close to each other ,and there is a lot of outliers since the dataset represents the global sales of video games.
boxplot(dataset$Global_Sales , main="BoxPlot for Global_Sales")
<<<<<<< Updated upstream
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The boxplot of the Global-sales attribute indicate that the values are close to each other ,and there is a lot of outliers since the dataset represents the global sales of video games.
qplot(data = dataset, x=Global_Sales,y=Genre,fill=I("yellow"),width=0.5 ,geom = "boxplot" , main = "BoxPlots for genre and Global_Sales")
<<<<<<< Updated upstream
=======
Warning: `qplot()` was deprecated in ggplot2 3.4.0.
In the boxplot we can see that all the genres have Glob_ sales close to each other, but we notice an outlier that reaches more than 80 Glob_ sales which is a game with genre sports.
dataset$Year %>% table() %>% barplot( main = "Barplot for year")
<<<<<<< Updated upstream
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The barplot of year illustrate that the number of video games were low from 1980 until 2000 , then number of games increased to more than 1200 till 2012.
pairs(~NA_Sales + EU_Sales + JP_Sales + Other_Sales + Global_Sales, data = dataset,
main = "Sales Scatterplot")
<<<<<<< Updated upstream
=======
We used Scatterplot to determine the type of correlation we have between the sales; we can see that the majority have positive correlation with each other.
dataset$Rank=as.character(dataset$Rank)
We transformed the Rank from numric to char,because we will use them as ordinal data.
we checked nulls values to know how many nulls values we have, so we can determine how we will deal with them.
<<<<<<< Updated upstreamsum(is.na(dataset$Rank))
NullRank<-dataset[dataset$Rank=="N/A",]
NullRank
=======
sum(is.na(dataset$Rank))
[1] 0
NullRank<-dataset[dataset$Rank=="N/A",]
NullRank
checking for nulls in Rank (there is no nulls)
<<<<<<< Updated upstreamsum(is.na(dataset$Name))
NullName<-dataset[dataset$Name=="N/A",]
NullName
=======
sum(is.na(dataset$Name))
[1] 0
NullName<-dataset[dataset$Name=="N/A",]
NullName
checking for nulls in name (there is no nulls)
<<<<<<< Updated upstreamsum(is.na(dataset$Platform))
NullPlatform<-dataset[dataset$Platform=="N/A",]
=======
sum(is.na(dataset$Platform))
[1] 0
NullPlatform<-dataset[dataset$Platform=="N/A",]
>>>>>>> Stashed changes
checking for nulls in Platform(there is no nulls)
<<<<<<< Updated upstream
sum(is.na(dataset$Year))
NullYear<-dataset[dataset$Year=="N/A",]
NullYear
=======
sum(is.na(dataset$Year))
[1] 0
NullYear<-dataset[dataset$Year=="N/A",]
NullYear
>>>>>>> Stashed changes
checking for nulls in year we won’t delete the null and we will leave
them as global constant because we want the sales data out of them.
<<<<<<< Updated upstream
sum(is.na(dataset$Genre))
NullGenre<-dataset[dataset$Genre=="N/A",]
NullGenre
=======
sum(is.na(dataset$Genre))
[1] 0
NullGenre<-dataset[dataset$Genre=="N/A",]
NullGenre
>>>>>>> Stashed changes
checking for nulls in Genre(there is no nulls)
<<<<<<< Updated upstream
sum(is.na(dataset$Publisher))
NullPublisher<-dataset[dataset$Publisher=="N/A",]
NullPublisher
=======
sum(is.na(dataset$Publisher))
[1] 0
NullPublisher<-dataset[dataset$Publisher=="N/A",]
NullPublisher
>>>>>>> Stashed changes
checking for nulls in Publisher. we won’t delete the null and we will
leave them as global constant as it is because we want the sales data of
them.
<<<<<<< Updated upstream
sum(is.na(dataset$NA_Sales))
NullNA_Sales<-dataset[dataset$NA_Sales=="N/A",]
NullNA_Sales
=======
sum(is.na(dataset$NA_Sales))
[1] 0
NullNA_Sales<-dataset[dataset$NA_Sales=="N/A",]
NullNA_Sales
>>>>>>> Stashed changes
checking for nulls in NA_Sales (there is no nulls)
<<<<<<< Updated upstream
sum(is.na(dataset$EU_Sales))
NullEU_Sales<-dataset[dataset$EU_Sales=="N/A",]
NullEU_Sales
=======
sum(is.na(dataset$EU_Sales))
[1] 0
NullEU_Sales<-dataset[dataset$EU_Sales=="N/A",]
NullEU_Sales
>>>>>>> Stashed changes
checking for nulls in EU_Sales (there is no nulls)
<<<<<<< Updated upstream
sum(is.na(dataset$JP_Sales))
NullJP_Sales<-dataset[dataset$JP_Sales=="N/A",]
NullJP_Sales
=======
sum(is.na(dataset$JP_Sales))
[1] 0
NullJP_Sales<-dataset[dataset$JP_Sales=="N/A",]
NullJP_Sales
>>>>>>> Stashed changes
checking for nulls in JP_Sales (there is no nulls)
<<<<<<< Updated upstream
sum(is.na(dataset$Other_Sales))
NullOther_Sales<-dataset[dataset$Other_Sales=="N/A",]
=======
sum(is.na(dataset$Other_Sales))
[1] 0
NullOther_Sales<-dataset[dataset$Other_Sales=="N/A",]
>>>>>>> Stashed changes
There is no null values in the other_sales.
<<<<<<< Updated upstream
sum(is.na(dataset$Global_Sales))
NullGlobal_Sales<-dataset[dataset$Global_Saless=="N/A",]
=======
sum(is.na(dataset$Global_Sales))
[1] 0
NullGlobal_Sales<-dataset[dataset$Global_Saless=="N/A",]
>>>>>>> Stashed changes
There is no null values in the Global_Sales.
We will encode our categorical data since most machine learning algorithms work with numbers rather than text.
dataset$Platform=factor(dataset$Platform,levels=c("2600","3DO","3DS","DC","DS","GB","GBA","GC","GEN","GG","N64","NES","NG","PC","PCFX","PS","PS2","PS3","PS4","PSP","PSV","SAT","SCD","SNES","TG16","Wii","WiiU","WS","X360","XB","XOne"), labels=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31))
this column will be encoded to facilitate our data mining task.
dataset$Genre=factor(dataset$Genre,levels=c("Action","Adventure","Fighting","Platform","Puzzle","Racing","Role-Playing","Shooter","Simulation","Sports","Strategy","Misc"),labels=c(1,2,3,4,5,6,7,8,9,10,11,12))
Since most machine learning algorithms work with numbers and not with text or categorical variables, this column will be encoded to facilitate our data mining task.
Analyses and statistical models can be ruined by outliers, making it difficult to detect a true effect. Therefore, we are checking for them and removing them if we find any.
outlier of NA_Sales
<<<<<<< Updated upstreamOutNA_Sales = outlier(dataset$NA_Sales, logical =TRUE)
sum(OutNA_Sales)
Find_outlier = which(OutNA_Sales ==TRUE, arr.ind = TRUE)
=======
OutNA_Sales = outlier(dataset$NA_Sales, logical =TRUE)
sum(OutNA_Sales)
[1] 1
Find_outlier = which(OutNA_Sales ==TRUE, arr.ind = TRUE)
>>>>>>> Stashed changes
outlier of EU_Sales
<<<<<<< Updated upstream
OutEU_Sales = outlier(dataset$EU_Sales, logical =TRUE)
sum(OutEU_Sales)
Find_outlier = which(OutEU_Sales ==TRUE, arr.ind = TRUE)
=======
OutEU_Sales = outlier(dataset$EU_Sales, logical =TRUE)
sum(OutEU_Sales)
[1] 1
Find_outlier = which(OutEU_Sales ==TRUE, arr.ind = TRUE)
>>>>>>> Stashed changes
outlier of JP_Sales
<<<<<<< Updated upstream
OutJP_Sales = outlier(dataset$JP_Sales, logical =TRUE)
sum(OutJP_Sales)
Find_outlier = which(OutJP_Sales ==TRUE, arr.ind = TRUE)
=======
OutJP_Sales = outlier(dataset$JP_Sales, logical =TRUE)
sum(OutJP_Sales)
[1] 1
Find_outlier = which(OutJP_Sales ==TRUE, arr.ind = TRUE)
>>>>>>> Stashed changes
outlier of other_sales
<<<<<<< Updated upstream
OutOS=outlier(dataset$Other_Sales, logical=TRUE)
sum(OutOS)
Find_outlier=which(OutOS==TRUE, arr.ind=TRUE)
=======
OutOS=outlier(dataset$Other_Sales, logical=TRUE)
sum(OutOS)
[1] 1
Find_outlier=which(OutOS==TRUE, arr.ind=TRUE)
>>>>>>> Stashed changes
outlier of Global_sales
<<<<<<< Updated upstream
OutGS=outlier(dataset$Global_Sales, logical=TRUE)
sum(OutGS)
Find_outlier=which(OutGS==TRUE, arr.ind=TRUE)
=======
OutGS=outlier(dataset$Global_Sales, logical=TRUE)
sum(OutGS)
[1] 1
Find_outlier=which(OutGS==TRUE, arr.ind=TRUE)
>>>>>>> Stashed changes
dataset= dataset[-Find_outlier,]
The normalization of data will improve the performance of many machine learning algorithms by accounting for differences in the scale of the input features.
Dataset before normalization:
datsetWithoutNormalization<-dataset
normalize <- function(x) {return ((x - min(x)) / (max(x) - min(x)))}
dataset$NA_Sales<-normalize(datsetWithoutNormalization$NA_Sales)
dataset$EU_Sales<-normalize(datsetWithoutNormalization$EU_Sales)
dataset$JP_Sales<-normalize(datsetWithoutNormalization$JP_Sales)
dataset$Other_Sales<-normalize(datsetWithoutNormalization$Other_Sales)
dataset$Global_Sales<-normalize(datsetWithoutNormalization$Global_Sales)
We chose min-max normalization instead of z-score normalization because min-max transform the data into a specific range, which enhances its suitability for visualization and comparison. Additionally, it simplifies the process of assessing attribute importance and their contributions to the model.
Our class label (popular) refers to Global_Sales.because we have multiple regions sales we chose to evaluate each region sales based on their importance to (global_sales) column,and those that are less important will be deleted from the dataset.
Use roc_curve area as score
roc_imp <- filterVarImp(x = dataset[,7:10], y = dataset$Global_Sales)
Sort the score in decreasing order
roc_imp <- data.frame(cbind(variable = rownames(roc_imp), score = roc_imp[,1]))
roc_imp$score <- as.double(roc_imp$score)
roc_imp[order(roc_imp$score,decreasing = TRUE),]
<<<<<<< Updated upstream
=======
we will remove the (JP_Sales) because it is of low importance to our class_label(Global_Sales)
dataset<- dataset[,-9]
#Discritization
dataBeforDiscertize=(dataset[,7:10])
library("arules")
dataAfterDiscertize=discretizeDF(
dataBeforDiscertize)
unique(dataAfterDiscertize[,1])
unique(dataAfterDiscertize[,2])
unique(dataAfterDiscertize[,3])
unique(dataAfterDiscertize[,4])
levels(dataAfterDiscertize$NA_Sales)<-c("low","medium","high")
levels(dataAfterDiscertize$EU_Sales)<-c("low","medium","high")
levels(dataAfterDiscertize$Other_Sales)<-c("low","medium","high")
levels(dataAfterDiscertize$Global_Sales)<-c("low","medium","high")
#Balancing
dataset[,7:10]=dataAfterDiscertize[,1:4]
=======
#Discritization
dataBeforDiscertize=(dataset[,7:10])
library("arules")
Loading required package: Matrix
Attaching package: ‘arules’
The following object is masked from ‘package:dplyr’:
recode
The following objects are masked from ‘package:base’:
abbreviate, write
dataAfterDiscertize=discretizeDF(
dataBeforDiscertize)
Warning: The calculated breaks are: 0, 0, 0.0046583850931677, 1
Only unique breaks are used reducing the number of intervals. Look at ? discretize for details.Warning: The calculated breaks are: 0, 0, 0.00189214758751183, 1
Only unique breaks are used reducing the number of intervals. Look at ? discretize for details.
unique(dataAfterDiscertize[,1])
[1] [0.0055,1] [0.000688,0.0055) [0,0.000688)
Levels: [0,0.000688) [0.000688,0.0055) [0.0055,1]
unique(dataAfterDiscertize[,2])
[1] [0.00466,1] [0,0.00466)
Levels: [0,0.00466) [0.00466,1]
unique(dataAfterDiscertize[,3])
[1] [0.00189,1] [0,0.00189)
Levels: [0,0.00189) [0.00189,1]
unique(dataAfterDiscertize[,4])
[1] [0.00795,1] [0.00199,0.00795) [0,0.00199)
Levels: [0,0.00199) [0.00199,0.00795) [0.00795,1]
levels(dataAfterDiscertize$NA_Sales)<-c("low","medium","high")
levels(dataAfterDiscertize$EU_Sales)<-c("low","high")
levels(dataAfterDiscertize$Other_Sales)<-c("low","high")
levels(dataAfterDiscertize$Global_Sales)<-c("low","medium","high")
#Balancing
>>>>>>> Stashed changes
library(groupdata2)
dataset<-downsample(dataset,cat_col="Global_Sales")
print(dataset)
<<<<<<< Updated upstream
=======
We performed balancing and discritization because we notice from the graphs that our dataset is inbalanced.
The goal of classification is to build a model or algorithm that can generalize patterns and relationships observed in the training data to make accurate predictions on unseen data. The model learns from the labeled examples in the training set, where each example consists of a set of input features and a corresponding known class label.
The ID3 (Iterative Dichotomiser 3) algorithm utilizes information gain as a key criterion for splitting nodes in the tree. It was developed by Ross Quinlan and is one of the earliest algorithms for building decision trees.In order to construct a decision tree, each node must be selected in a recursive manner for the maximum Information Gain. At each step, the algorithm selects the attribute with the highest Information Gain to split the data, and this process continues until a stopping criterion is reached.
set.seed(15687)
ind <- sample(2, nrow(dataset), replace=TRUE, prob=c(0.9, 0.1))
trainData <- dataset[ind==1,]
testData <- dataset[ind==2,]
library(party)
myFormula <- Global_Sales ~ NA_Sales +EU_Sales+Other_Sales+Genre+Platform
dataset_ctree <- ctree(myFormula, data=trainData)
<<<<<<< Updated upstream
table(predict(dataset_ctree), trainData$Global_Sales)
plot(dataset_ctree,type="simple")
plot(dataset_ctree)
testPred <- predict(dataset_ctree, newdata = testData)
result<-table(testPred, testData$Global_Sales)
result
library(e1071)
library(caret)
co_result <- confusionMatrix(result)
print(co_result)
acc90ctree <- co_result$overall["Accuracy"]
acc90ctree=acc90ctree*100
=======
table(predict(dataset_ctree), trainData$Global_Sales)
0 0.000248570718369376 0.000497141436738752 0.000745712155108128 0.000994282873477504 0.00124285359184688 0.00149142431021626
0.0159141753101485 1 1 1 1 1 1 1
0.0219985085756898 0 0 0 0 0 0 0
0.0303256276410639 0 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0 0
0.0579666915237385 0 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0 0
0.0922907567202869 0 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 0 0
0.113555389841743 0 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0 0
0.246608317961197 0 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0 0
0.00173999502858563 0.00198856574695501 0.00223713646532438 0.00248570718369376 0.00273427790206314 0.00298284862043251
0.0159141753101485 1 1 1 1 1 1
0.0219985085756898 0 0 0 0 0 0
0.0303256276410639 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0
0.0579666915237385 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0
0.0922907567202869 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 0
0.113555389841743 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0
0.246608317961197 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0
0.00323141933880189 0.00347999005717126 0.00372856077554064 0.00397713149391002 0.00422570221227939 0.00447427293064877
0.0159141753101485 1 1 1 1 1 1
0.0219985085756898 0 0 0 0 0 0
0.0303256276410639 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0
0.0579666915237385 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0
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0.542132736763609 0.546607009694258 0.564504101416853 0.573949788714889 0.581904051702709 0.615212527964206 0.703455132985334
0.0159141753101485 0 0 0 0 0 0 0
0.0219985085756898 0 0 0 0 0 0 0
0.0303256276410639 0 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0 0
0.0579666915237385 0 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0 0
0.0922907567202869 0 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 0 0
0.113555389841743 0 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0 0
0.246608317961197 0 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0 0
0.602653165351858 1 1 1 1 1 1 1
0.711160825254785 0.72110365398956 0.745712155108128 0.751926423067363 0.820034799900572 0.890131742480736
0.0159141753101485 0 0 0 0 0 0
0.0219985085756898 0 0 0 0 0 0
0.0303256276410639 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0
0.0579666915237385 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0
0.0922907567202869 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 0
0.113555389841743 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0
0.246608317961197 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0
0.602653165351858 1 1 1 1 1 1
print(dataset_ctree)
Conditional inference tree with 25 terminal nodes
Response: Global_Sales
Inputs: NA_Sales, EU_Sales, Other_Sales, Genre, Platform
Number of observations: 565
1) NA_Sales <= 0.1712517; criterion = 1, statistic = 472.848
2) NA_Sales <= 0.08700138; criterion = 1, statistic = 342.942
3) EU_Sales <= 0.05279503; criterion = 1, statistic = 228.952
4) NA_Sales <= 0.04126547; criterion = 1, statistic = 127.032
5) NA_Sales <= 0.0147868; criterion = 1, statistic = 43.789
6) Other_Sales <= 0.01135289; criterion = 0.984, statistic = 22.266
7)* weights = 88
6) Other_Sales > 0.01135289
8)* weights = 8
5) NA_Sales > 0.0147868
9) Other_Sales <= 0.0217597; criterion = 0.994, statistic = 31.281
10) NA_Sales <= 0.01994498; criterion = 0.997, statistic = 33.853
11) EU_Sales <= 0.02562112; criterion = 0.998, statistic = 12.373
12)* weights = 14
11) EU_Sales > 0.02562112
13)* weights = 9
10) NA_Sales > 0.01994498
14)* weights = 52
9) Other_Sales > 0.0217597
15)* weights = 10
4) NA_Sales > 0.04126547
16) Other_Sales <= 0.03027436; criterion = 1, statistic = 21.431
17) NA_Sales <= 0.07496561; criterion = 1, statistic = 16.839
18) NA_Sales <= 0.05295736; criterion = 0.982, statistic = 17.307
19)* weights = 15
18) NA_Sales > 0.05295736
20)* weights = 25
17) NA_Sales > 0.07496561
21)* weights = 9
16) Other_Sales > 0.03027436
22)* weights = 7
3) EU_Sales > 0.05279503
23) EU_Sales <= 0.125; criterion = 1, statistic = 66.484
24) NA_Sales <= 0.04367263; criterion = 1, statistic = 54.45
25)* weights = 47
24) NA_Sales > 0.04367263
26) NA_Sales <= 0.06327373; criterion = 0.999, statistic = 18.673
27)* weights = 37
26) NA_Sales > 0.06327373
28)* weights = 37
23) EU_Sales > 0.125
29) EU_Sales <= 0.2437888; criterion = 0.997, statistic = 12.757
30) NA_Sales <= 0.053989; criterion = 1, statistic = 17.772
31)* weights = 23
30) NA_Sales > 0.053989
32)* weights = 20
29) EU_Sales > 0.2437888
33)* weights = 7
2) NA_Sales > 0.08700138
34) EU_Sales <= 0.2026398; criterion = 1, statistic = 62.344
35) EU_Sales <= 0.1304348; criterion = 1, statistic = 20.714
36) Other_Sales <= 0.05581835; criterion = 1, statistic = 25.924
37) NA_Sales <= 0.1000688; criterion = 0.981, statistic = 23.686
38)* weights = 18
37) NA_Sales > 0.1000688
39)* weights = 25
36) Other_Sales > 0.05581835
40)* weights = 8
35) EU_Sales > 0.1304348
41)* weights = 33
34) EU_Sales > 0.2026398
42) EU_Sales <= 0.238354; criterion = 0.967, statistic = 13.681
43)* weights = 10
42) EU_Sales > 0.238354
44)* weights = 10
1) NA_Sales > 0.1712517
45) NA_Sales <= 0.2929849; criterion = 1, statistic = 32.036
46) EU_Sales <= 0.2414596; criterion = 1, statistic = 24.666
47)* weights = 19
46) EU_Sales > 0.2414596
48)* weights = 15
45) NA_Sales > 0.2929849
49)* weights = 19
plot(dataset_ctree,type="simple")
plot(dataset_ctree)
testPred <- predict(dataset_ctree, newdata = testData)
result<-table(testPred, testData$Global_Sales)
result
testPred 0.0062142679592344 0.0136713895103157 0.0161570966940094 0.0181456624409645 0.0196370867511807 0.0201342281879195
0.0159141753101485 1 1 1 1 0 0
0.0219985085756898 0 0 0 0 0 1
0.0319102659706686 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 1 0
0.0526141353881846 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 0
0.113555389841743 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0
testPred 0.021128511061397 0.0268456375838926 0.033059905543127 0.0352970420084514 0.0420084514044246 0.0430027342779021
0.0159141753101485 1 0 0 0 0 0
0.0219985085756898 0 0 0 0 0 0
0.0319102659706686 0 1 0 0 0 0
0.0395370848390983 0 0 1 1 0 1
0.0526141353881846 0 0 0 0 0 0
0.059260317006997 0 0 0 0 1 0
0.0634352473278648 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 0
0.113555389841743 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0
testPred 0.0432513049962714 0.0474770072085508 0.0479741486452896 0.0546855580412627 0.056176982351479 0.0584141188168034
0.0159141753101485 0 0 0 0 0 0
0.0219985085756898 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0
0.0395370848390983 1 0 0 0 0 1
0.0526141353881846 0 0 1 1 1 0
0.059260317006997 0 1 0 0 0 0
0.0634352473278648 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 0
0.113555389841743 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0
testPred 0.0626398210290828 0.0633855331841909 0.0666169525229928 0.0700969425801641 0.0743226447924435 0.0755654983842903
0.0159141753101485 0 0 0 0 0 0
0.0219985085756898 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0
0.0634352473278648 0 1 0 0 0 0
0.0822097265050285 0 0 0 1 0 0
0.0828292871544176 1 0 1 0 1 0
0.0954511558538404 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 1
0.113555389841743 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0
testPred 0.0785483470047228 0.0797912005965697 0.081282624906786 0.085259756400696 0.0897340293313448 0.0909768829231916
0.0159141753101485 0 1 0 0 0 1
0.0219985085756898 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0
0.0634352473278648 1 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0
0.099314079179851 0 0 1 1 1 0
0.113555389841743 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0
testPred 0.0914740243599304 0.0944568729803629 0.0999254287844892 0.101665423813075 0.105145413870246 0.120059656972409
0.0159141753101485 0 0 0 0 0 0
0.0219985085756898 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0
0.0822097265050285 1 1 0 0 0 0
0.0828292871544176 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 1 0
0.099314079179851 0 0 0 1 0 0
0.113555389841743 0 0 1 0 0 0
0.132975391498881 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 1
0.173961840628507 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0
testPred 0.122545364156102 0.130748197862292 0.132985334327616 0.138205319413373 0.156350981854338 0.156599552572707 0.157345264727815
0.0159141753101485 0 0 0 0 0 0 0
0.0219985085756898 0 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0 0
0.0822097265050285 0 0 1 0 0 0 0
0.0828292871544176 0 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0 0
0.099314079179851 0 0 0 0 1 0 0
0.113555389841743 0 0 0 0 0 0 0
0.132975391498881 0 1 0 0 0 1 0
0.135384041759881 1 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0 0
0.173961840628507 0 0 0 1 0 0 1
0.212329107631121 0 0 0 0 0 0 0
0.280462341536167 0 0 0 0 0 0 0
0.360957825834783 0 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0 0
testPred 0.162813820531941 0.166045239870743 0.169028088491176 0.190405170270942 0.204573701217997 0.206810837683321 0.217499378573204
0.0159141753101485 0 0 0 0 0 0 0
0.0219985085756898 0 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 0 0
0.113555389841743 0 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0 1
0.135384041759881 0 0 0 0 0 0 0
0.152125279642058 0 1 0 0 1 0 0
0.15243599304002 0 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 1 0
0.212329107631121 1 0 1 0 0 0 0
0.280462341536167 0 0 0 1 0 0 0
0.360957825834783 0 0 0 0 0 0 0
0.602653165351858 0 0 0 0 0 0 0
testPred 0.238130748197862 0.25478498632861 0.353716132239622 0.393736017897092 0.502361421824509 0.779517772806363 1
0.0159141753101485 0 0 0 0 0 0 0
0.0219985085756898 0 0 0 0 0 0 0
0.0319102659706686 0 0 0 0 0 0 0
0.0395370848390983 0 0 0 0 0 0 0
0.0526141353881846 0 0 0 0 0 0 0
0.059260317006997 0 0 0 0 0 0 0
0.0634352473278648 0 0 0 0 0 0 0
0.0822097265050285 0 0 0 0 0 0 0
0.0828292871544176 0 0 0 0 0 0 0
0.0954511558538404 0 0 0 0 0 0 0
0.099314079179851 0 0 0 0 0 0 0
0.113555389841743 0 0 0 0 0 0 0
0.132975391498881 0 0 0 0 0 0 0
0.135384041759881 0 0 0 0 0 0 0
0.152125279642058 0 0 0 0 0 0 0
0.15243599304002 0 0 0 0 0 0 0
0.173961840628507 0 0 0 0 0 0 0
0.212329107631121 0 0 0 0 0 0 0
0.280462341536167 1 0 0 0 1 0 0
0.360957825834783 0 1 1 1 0 0 0
0.602653165351858 0 0 0 0 0 1 1
library(e1071)
library(caret)
co_result <- confusionMatrix(result)
Error in !all.equal(nrow(data), ncol(data)) : invalid argument type
>>>>>>> Stashed changes
<<<<<<< Updated upstream
precision90ctree=(((442/862)+(281/916)+(454/1010))/3)*100
sensitivity90ctree=(( 0.8080+0.5165+0.8731)/3)*100
specificity90ctree=(( 0.8102+0.8585+0.9258)/3)*100
=======
precision90ctree=(((486/808)+(243/919)+(422/974))/3)*100
sensitivity90ctree=(( 0.8764+0.4559+0.8423)/3)*100
specificity90ctree=((0.7814+0.8879+0.9128)/3)*100
>>>>>>> Stashed changes
set.seed(15687)
ind <- sample(2, nrow(dataset), replace=TRUE, prob=c(0.8, 0.2))
trainData <- dataset[ind==1,]
testData <- dataset[ind==2,]
library(party)
myFormula <- Global_Sales ~ NA_Sales +EU_Sales+Other_Sales+Genre+Platform
dataset_ctree <- ctree(myFormula, data=trainData)
<<<<<<< Updated upstream
table(predict(dataset_ctree), trainData$Global_Sales)
print(dataset_ctree)
plot(dataset_ctree,type="simple")
plot(dataset_ctree)
testPred <- predict(dataset_ctree, newdata = testData)
result<-table(testPred, testData$Global_Sales)
result
library(e1071)
library(caret)
co_result <- confusionMatrix(result)
print(co_result)
acc80ctree <- co_result$overall["Accuracy"]
acc80ctree=acc80ctree*100
=======
table(predict(dataset_ctree), trainData$Global_Sales)
0 0.000248570718369376 0.000745712155108128 0.000994282873477504 0.00124285359184688 0.00149142431021626 0.00173999502858563
0.0162225100409488 1 1 1 1 1 1 1
0.0251187252246948 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0 0
0.0579666915237385 0 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0 0
0.0922907567202869 0 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0 0
0.00198856574695501 0.00223713646532438 0.00248570718369376 0.00273427790206314 0.00298284862043251 0.00323141933880189
0.0162225100409488 1 1 1 1 1 1
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0579666915237385 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0
0.0922907567202869 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
0.00347999005717126 0.00372856077554064 0.00397713149391002 0.00472284364901815 0.00497141436738752 0.0052199850857569
0.0162225100409488 1 1 1 1 1 1
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0579666915237385 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0
0.0922907567202869 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
0.00571712652249565 0.00596569724086503 0.00646283867760378 0.00671140939597315 0.00695998011434253 0.00720855083271191
0.0162225100409488 1 1 1 1 1 1
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0579666915237385 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0
0.0922907567202869 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
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0.641259188239054 1 1
print(dataset_ctree)
Conditional inference tree with 24 terminal nodes
Response: Global_Sales
Inputs: NA_Sales, EU_Sales, Other_Sales, Genre, Platform
Number of observations: 504
1) NA_Sales <= 0.1712517; criterion = 1, statistic = 422.526
2) NA_Sales <= 0.08700138; criterion = 1, statistic = 312.701
3) EU_Sales <= 0.05279503; criterion = 1, statistic = 200.311
4) NA_Sales <= 0.04126547; criterion = 1, statistic = 109.353
5) NA_Sales <= 0.0147868; criterion = 1, statistic = 52.026
6) Other_Sales <= 0.01135289; criterion = 0.972, statistic = 25.987
7)* weights = 76
6) Other_Sales > 0.01135289
8)* weights = 7
5) NA_Sales > 0.0147868
9) Other_Sales <= 0.0217597; criterion = 0.989, statistic = 33.256
10) NA_Sales <= 0.01994498; criterion = 0.995, statistic = 32.775
11)* weights = 19
10) NA_Sales > 0.01994498
12)* weights = 49
9) Other_Sales > 0.0217597
13)* weights = 10
4) NA_Sales > 0.04126547
14) Other_Sales <= 0.03027436; criterion = 1, statistic = 22.106
15) NA_Sales <= 0.06877579; criterion = 1, statistic = 16.377
16)* weights = 31
15) NA_Sales > 0.06877579
17)* weights = 13
14) Other_Sales > 0.03027436
18)* weights = 7
3) EU_Sales > 0.05279503
19) EU_Sales <= 0.1335404; criterion = 1, statistic = 55.711
20) NA_Sales <= 0.05295736; criterion = 1, statistic = 46.184
21) NA_Sales <= 0.02200825; criterion = 0.978, statistic = 15.222
22)* weights = 12
21) NA_Sales > 0.02200825
23)* weights = 49
20) NA_Sales > 0.05295736
24)* weights = 49
19) EU_Sales > 0.1335404
25) EU_Sales <= 0.1785714; criterion = 0.975, statistic = 17.399
26) NA_Sales <= 0.053989; criterion = 1, statistic = 16.081
27)* weights = 14
26) NA_Sales > 0.053989
28)* weights = 13
25) EU_Sales > 0.1785714
29)* weights = 16
2) NA_Sales > 0.08700138
30) EU_Sales <= 0.1878882; criterion = 1, statistic = 50.162
31) Other_Sales <= 0.04351939; criterion = 1, statistic = 17.802
32) EU_Sales <= 0.1273292; criterion = 1, statistic = 23.663
33) NA_Sales <= 0.105227; criterion = 0.968, statistic = 20.556
34)* weights = 21
33) NA_Sales > 0.105227
35)* weights = 13
32) EU_Sales > 0.1273292
36)* weights = 10
31) Other_Sales > 0.04351939
37) Other_Sales <= 0.06433302; criterion = 0.996, statistic = 11.072
38)* weights = 15
37) Other_Sales > 0.06433302
39)* weights = 13
30) EU_Sales > 0.1878882
40)* weights = 20
1) NA_Sales > 0.1712517
41) NA_Sales <= 0.3105227; criterion = 1, statistic = 28.438
42) EU_Sales <= 0.2864907; criterion = 1, statistic = 25.162
43) EU_Sales <= 0.1847826; criterion = 0.981, statistic = 12.568
44)* weights = 9
43) EU_Sales > 0.1847826
45)* weights = 12
42) EU_Sales > 0.2864907
46)* weights = 12
41) NA_Sales > 0.3105227
47)* weights = 14
plot(dataset_ctree,type="simple")
plot(dataset_ctree)
testPred <- predict(dataset_ctree, newdata = testData)
result<-table(testPred, testData$Global_Sales)
result
testPred 0.000497141436738752 0.00422570221227939 0.00447427293064877 0.00546855580412627 0.0062142679592344 0.00745712155108128
0.0162225100409488 1 1 1 1 1 1
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.0114342530449913 0.0134228187919463 0.0136713895103157 0.0139199602286851 0.0146656723837932 0.0154113845389013
0.0162225100409488 0 1 1 1 1 1
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 1 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.0161570966940094 0.0169028088491176 0.0181456624409645 0.0186428038777032 0.0196370867511807 0.0201342281879195
0.0162225100409488 1 1 1 0 0 0
0.0251187252246948 0 0 0 1 0 1
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 0 0 1 0
0.0486991465738669 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.021128511061397 0.0218742232165051 0.0223713646532438 0.0268456375838926 0.0283370618941089 0.0285856326124782
0.0162225100409488 1 0 0 0 0 0
0.0251187252246948 0 0 1 0 0 1
0.0348354106743368 0 0 0 1 0 0
0.039735804836476 0 1 0 0 0 0
0.0486991465738669 0 0 0 0 1 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.0315684812329108 0.033059905543127 0.0340541884166045 0.0347999005717126 0.0352970420084514 0.0382798906288839
0.0162225100409488 1 0 0 0 0 0
0.0251187252246948 0 0 1 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 1 0 1 1 0
0.0486991465738669 0 0 0 0 0 1
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.0395227442207308 0.0420084514044246 0.0430027342779021 0.0432513049962714 0.0474770072085508 0.0479741486452896
0.0162225100409488 1 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 1 1 0 0
0.0486991465738669 0 1 0 0 0 0
0.0581254560470841 0 0 0 0 0 1
0.0693055745788248 0 0 0 0 1 0
0.0755272567353104 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.048222719363659 0.052199850857569 0.0524484215759384 0.054188416604524 0.0546855580412627 0.0549341287596321
0.0162225100409488 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 1 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0581254560470841 1 0 0 0 1 1
0.0693055745788248 0 0 1 1 0 0
0.0755272567353104 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.056176982351479 0.0584141188168034 0.0591598309719115 0.0626398210290828 0.0633855331841909 0.0658712403678847
0.0162225100409488 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 1 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0581254560470841 1 0 0 0 0 1
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 0 0 1 1 1 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.0666169525229928 0.0700969425801641 0.0730797912005966 0.0743226447924435 0.0750683569475516 0.0755654983842903
0.0162225100409488 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 1 0 0 1 0
0.0755272567353104 1 0 0 1 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 0 0 1 0 0 1
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.0785483470047228 0.0795426298782003 0.0797912005965697 0.081282624906786 0.085259756400696 0.0855083271190654
0.0162225100409488 0 0 1 0 0 0
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0
0.0755272567353104 1 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 1
0.0965266046761732 0 1 0 1 1 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.0869997514292816 0.0897340293313448 0.0907283122048223 0.0909768829231916 0.0914740243599304 0.0922197365150385
0.0162225100409488 0 0 0 1 0 0
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 1
0.0755272567353104 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0
0.0965266046761732 1 1 1 0 1 0
0.117668643395713 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.0934625901068854 0.0944568729803629 0.097191150882426 0.0999254287844892 0.101665423813075 0.104648272433507
0.0162225100409488 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0
0.0693055745788248 1 1 0 0 0 0
0.0755272567353104 0 0 0 0 0 0
0.0946166684421718 0 0 1 0 0 0
0.0965266046761732 0 0 0 0 1 1
0.117668643395713 0 0 0 1 0 0
0.124055909290809 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0
testPred 0.105145413870246 0.105642555306985 0.106885408898832 0.108625403927417 0.108873974645787 0.11135968182948 0.11160825254785
0.0162225100409488 0 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0 0
0.0755272567353104 0 0 0 1 0 0 0
0.0946166684421718 1 0 0 0 0 0 0
0.0965266046761732 0 0 1 0 0 0 0
0.117668643395713 0 1 0 0 1 0 0
0.124055909290809 0 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0 1
0.155671555224128 0 0 0 0 0 1 0
0.175012906556531 0 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0 0
testPred 0.118816803380562 0.120059656972409 0.121053939845886 0.122545364156102 0.130748197862292 0.132985334327616 0.13547104151131
0.0162225100409488 0 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 1 0
0.117668643395713 1 0 1 0 0 0 0
0.124055909290809 0 0 0 1 0 0 0
0.132923191648024 0 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0 1
0.155671555224128 0 1 0 0 1 0 0
0.175012906556531 0 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0 0
testPred 0.136216753666418 0.138205319413373 0.140193885160328 0.156350981854338 0.156599552572707 0.157345264727815 0.158588118319662
0.0162225100409488 0 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0 0
0.0965266046761732 0 0 0 1 0 0 0
0.117668643395713 0 0 0 0 0 0 0
0.124055909290809 0 0 1 0 0 0 0
0.132923191648024 0 0 0 0 0 0 0
0.138090594466433 1 0 0 0 1 0 0
0.155671555224128 0 0 0 0 0 1 0
0.175012906556531 0 1 0 0 0 0 1
0.177802634849615 0 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0 0
0.286602038279891 0 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0 0
testPred 0.162813820531941 0.163808103405419 0.166045239870743 0.169028088491176 0.178722346507581 0.185185185185185 0.190405170270942
0.0162225100409488 0 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0 0
0.132923191648024 0 0 1 0 0 0 0
0.138090594466433 0 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 1 0
0.238590604026846 1 1 0 1 1 0 1
0.286602038279891 0 0 0 0 0 0 0
0.394730300770569 0 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0 0
testPred 0.19512801391996 0.201342281879195 0.204573701217997 0.206810837683321 0.217499378573204 0.219487944320159 0.238130748197862
0.0162225100409488 0 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0 0
0.0965266046761732 1 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0 0
0.132923191648024 0 1 1 0 0 0 0
0.138090594466433 0 0 0 0 1 0 0
0.155671555224128 0 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0 0
0.177802634849615 0 0 0 1 0 0 0
0.234374568453614 0 0 0 0 0 0 0
0.238590604026846 0 0 0 0 0 0 1
0.286602038279891 0 0 0 0 0 1 0
0.394730300770569 0 0 0 0 0 0 0
0.641259188239054 0 0 0 0 0 0 0
testPred 0.25478498632861 0.341039025602784 0.353716132239622 0.380064628386776 0.393736017897092 0.450658712403679 0.502361421824509
0.0162225100409488 0 0 0 0 0 0 0
0.0251187252246948 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.039735804836476 0 0 0 0 0 0 0
0.0486991465738669 0 0 0 0 0 0 0
0.0581254560470841 0 0 0 0 0 0 0
0.0693055745788248 0 0 0 0 0 0 0
0.0755272567353104 0 0 0 0 0 0 0
0.0946166684421718 0 0 0 0 0 0 0
0.0965266046761732 0 0 0 0 0 0 0
0.117668643395713 0 0 0 0 0 0 0
0.124055909290809 0 0 0 0 0 0 0
0.132923191648024 0 0 0 0 0 0 0
0.138090594466433 0 0 0 0 0 0 0
0.155671555224128 0 0 0 0 0 0 0
0.175012906556531 0 0 0 0 0 0 0
0.177802634849615 0 0 0 0 0 0 0
0.234374568453614 0 0 0 0 0 0 0
0.238590604026846 0 0 0 1 0 0 1
0.286602038279891 1 0 0 0 0 0 0
0.394730300770569 0 1 1 0 1 0 0
0.641259188239054 0 0 0 0 0 1 0
testPred 0.517027094208302 0.573949788714889 0.779517772806363 1
0.0162225100409488 0 0 0 0
0.0251187252246948 0 0 0 0
0.0348354106743368 0 0 0 0
0.039735804836476 0 0 0 0
0.0486991465738669 0 0 0 0
0.0581254560470841 0 0 0 0
0.0693055745788248 0 0 0 0
0.0755272567353104 0 0 0 0
0.0946166684421718 0 0 0 0
0.0965266046761732 0 0 0 0
0.117668643395713 0 0 0 0
0.124055909290809 0 0 0 0
0.132923191648024 0 0 0 0
0.138090594466433 0 0 0 0
0.155671555224128 0 0 0 0
0.175012906556531 0 0 0 0
0.177802634849615 0 0 0 0
0.234374568453614 0 0 0 0
0.238590604026846 0 0 0 0
0.286602038279891 0 0 0 0
0.394730300770569 0 1 0 0
0.641259188239054 1 0 1 1
library(e1071)
library(caret)
co_result <- confusionMatrix(result)
Error in !all.equal(nrow(data), ncol(data)) : invalid argument type
>>>>>>> Stashed changes
<<<<<<< Updated upstream
precision80ctree=(((930/1739)+(548/1890)+(927/2056))/3)*100
sensitivity80ctree=((0.8439+0.5037+ 0.8505)/3)*100
specificity80ctree=((0.7984+0.8622+0.9388)/3)*100
=======
precision80ctree=(((838/1746)+(578/1799)+(903/1974))/3)*100
sensitivity80ctree=(( 0.7766+0.5479+ 0.8471)/3)*100
specificity80ctree=(( 0.8232+0.8387+0.9250)/3)*100
>>>>>>> Stashed changes
set.seed(15687)
ind <- sample(2, nrow(dataset), replace=TRUE, prob=c(0.7, 0.3))
trainData <- dataset[ind==1,]
testData <- dataset[ind==2,]
library(party)
myFormula <- Global_Sales ~ NA_Sales +EU_Sales+Other_Sales+Genre+Platform
dataset_ctree <- ctree(myFormula, data=trainData)
<<<<<<< Updated upstream
table(predict(dataset_ctree), trainData$Global_Sales)
print(dataset_ctree)
plot(dataset_ctree,type="simple")
plot(dataset_ctree)
testPred <- predict(dataset_ctree, newdata = testData)
result<-table(testPred, testData$Global_Sales)
result
library(e1071)
library(caret)
co_result <- confusionMatrix(result)
print(co_result)
acc70ctree <- co_result$overall["Accuracy"]
acc70ctree=acc70ctree*100
=======
table(predict(dataset_ctree), trainData$Global_Sales)
0 0.000248570718369376 0.000745712155108128 0.000994282873477504 0.00124285359184688 0.00149142431021626 0.00173999502858563
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0.0379038477472482 0 0 0 0 0 0 0
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0.0528551736605428 0 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0 0
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0.143318774191257 0 0 0 0 0 0 0
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0.00198856574695501 0.00223713646532438 0.00248570718369376 0.00273427790206314 0.00298284862043251 0.00372856077554064
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0.00397713149391002 0.00472284364901815 0.00497141436738752 0.0052199850857569 0.00571712652249565 0.00596569724086503
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print(dataset_ctree)
Conditional inference tree with 24 terminal nodes
Response: Global_Sales
Inputs: NA_Sales, EU_Sales, Other_Sales, Genre, Platform
Number of observations: 442
1) NA_Sales <= 0.1712517; criterion = 1, statistic = 383.041
2) NA_Sales <= 0.08184319; criterion = 1, statistic = 276.504
3) EU_Sales <= 0.05590062; criterion = 1, statistic = 175.897
4) NA_Sales <= 0.03782669; criterion = 1, statistic = 93.97
5) NA_Sales <= 0.0147868; criterion = 1, statistic = 51.107
6) Other_Sales <= 0.01135289; criterion = 0.956, statistic = 27.043
7)* weights = 69
6) Other_Sales > 0.01135289
8)* weights = 7
5) NA_Sales > 0.0147868
9) Genre == {7, 9}; criterion = 0.986, statistic = 34.32
10)* weights = 9
9) Genre == {1, 2, 3, 4, 5, 6, 8, 10, 11, 12}
11) NA_Sales <= 0.01994498; criterion = 0.992, statistic = 24.332
12) EU_Sales <= 0.0302795; criterion = 0.988, statistic = 13.956
13)* weights = 14
12) EU_Sales > 0.0302795
14)* weights = 7
11) NA_Sales > 0.01994498
15)* weights = 39
4) NA_Sales > 0.03782669
16) Other_Sales <= 0.02838221; criterion = 1, statistic = 22.633
17) NA_Sales <= 0.05502063; criterion = 1, statistic = 18.191
18)* weights = 22
17) NA_Sales > 0.05502063
19)* weights = 20
16) Other_Sales > 0.02838221
20)* weights = 7
3) EU_Sales > 0.05590062
21) EU_Sales <= 0.1389752; criterion = 1, statistic = 47.989
22) NA_Sales <= 0.05089409; criterion = 1, statistic = 43.258
23) NA_Sales <= 0.02200825; criterion = 0.968, statistic = 20.381
24)* weights = 11
23) NA_Sales > 0.02200825
25)* weights = 35
22) NA_Sales > 0.05089409
26)* weights = 48
21) EU_Sales > 0.1389752
27)* weights = 29
2) NA_Sales > 0.08184319
28) EU_Sales <= 0.2274845; criterion = 1, statistic = 46.663
29) EU_Sales <= 0.1304348; criterion = 1, statistic = 18.929
30) Other_Sales <= 0.04351939; criterion = 1, statistic = 20.056
31) EU_Sales <= 0.07065217; criterion = 0.993, statistic = 18.974
32)* weights = 9
31) EU_Sales > 0.07065217
33) NA_Sales <= 0.105227; criterion = 0.954, statistic = 14.657
34)* weights = 14
33) NA_Sales > 0.105227
35)* weights = 7
30) Other_Sales > 0.04351939
36)* weights = 7
29) EU_Sales > 0.1304348
37) NA_Sales <= 0.09628611; criterion = 0.995, statistic = 22.938
38)* weights = 7
37) NA_Sales > 0.09628611
39)* weights = 27
28) EU_Sales > 0.2274845
40)* weights = 12
1) NA_Sales > 0.1712517
41) NA_Sales <= 0.3311554; criterion = 1, statistic = 28.488
42) EU_Sales <= 0.3501553; criterion = 1, statistic = 26.591
43) EU_Sales <= 0.2414596; criterion = 0.995, statistic = 12.511
44)* weights = 15
43) EU_Sales > 0.2414596
45)* weights = 9
42) EU_Sales > 0.3501553
46)* weights = 10
41) NA_Sales > 0.3311554
47)* weights = 8
plot(dataset_ctree,type="simple")
plot(dataset_ctree)
testPred <- predict(dataset_ctree, newdata = testData)
result<-table(testPred, testData$Global_Sales)
result
testPred 0.000497141436738752 0.00323141933880189 0.00347999005717126 0.00422570221227939 0.00447427293064877 0.00546855580412627
0.0165893935955214 1 1 1 1 1 1
0.0230993217570399 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0
0.0379038477472482 0 0 0 0 0 0
0.0497141436738752 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0
0.122416793094877 0 0 0 0 0 0
0.143318774191257 0 0 0 0 0 0
0.150882426050211 0 0 0 0 0 0
0.150917936152835 0 0 0 0 0 0
0.182855985490835 0 0 0 0 0 0
0.257237550749855 0 0 0 0 0 0
0.259735686469467 0 0 0 0 0 0
0.340182837572845 0 0 0 0 0 0
0.437633606761123 0 0 0 0 0 0
0.720513298533433 0 0 0 0 0 0
testPred 0.0062142679592344 0.00745712155108128 0.00770569226945066 0.00820283370618941 0.0114342530449913 0.0134228187919463
0.0165893935955214 1 1 1 1 0 1
0.0230993217570399 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 1 0
0.0366819360107951 0 0 0 0 0 0
0.0379038477472482 0 0 0 0 0 0
0.0497141436738752 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0
0.122416793094877 0 0 0 0 0 0
0.143318774191257 0 0 0 0 0 0
0.150882426050211 0 0 0 0 0 0
0.150917936152835 0 0 0 0 0 0
0.182855985490835 0 0 0 0 0 0
0.257237550749855 0 0 0 0 0 0
0.259735686469467 0 0 0 0 0 0
0.340182837572845 0 0 0 0 0 0
0.437633606761123 0 0 0 0 0 0
0.720513298533433 0 0 0 0 0 0
testPred 0.0136713895103157 0.0139199602286851 0.0146656723837932 0.0154113845389013 0.0161570966940094 0.0164056674123788
0.0165893935955214 1 1 1 1 1 1
0.0230993217570399 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0
0.0379038477472482 0 0 0 0 0 0
0.0497141436738752 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0
0.122416793094877 0 0 0 0 0 0
0.143318774191257 0 0 0 0 0 0
0.150882426050211 0 0 0 0 0 0
0.150917936152835 0 0 0 0 0 0
0.182855985490835 0 0 0 0 0 0
0.257237550749855 0 0 0 0 0 0
0.259735686469467 0 0 0 0 0 0
0.340182837572845 0 0 0 0 0 0
0.437633606761123 0 0 0 0 0 0
0.720513298533433 0 0 0 0 0 0
testPred 0.0169028088491176 0.0181456624409645 0.0186428038777032 0.0196370867511807 0.0201342281879195 0.021128511061397
0.0165893935955214 1 1 0 0 0 1
0.0230993217570399 0 0 1 0 1 0
0.0348354106743368 0 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0
0.0379038477472482 0 0 0 1 0 0
0.0497141436738752 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0
0.122416793094877 0 0 0 0 0 0
0.143318774191257 0 0 0 0 0 0
0.150882426050211 0 0 0 0 0 0
0.150917936152835 0 0 0 0 0 0
0.182855985490835 0 0 0 0 0 0
0.257237550749855 0 0 0 0 0 0
0.259735686469467 0 0 0 0 0 0
0.340182837572845 0 0 0 0 0 0
0.437633606761123 0 0 0 0 0 0
0.720513298533433 0 0 0 0 0 0
testPred 0.0218742232165051 0.0223713646532438 0.0228685060899826 0.0236142182450907 0.0253542132736764 0.0256027839920457
0.0165893935955214 0 0 0 1 0 1
0.0230993217570399 0 1 1 0 0 0
0.0348354106743368 0 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0
0.0379038477472482 1 0 0 0 1 0
0.0497141436738752 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0
0.122416793094877 0 0 0 0 0 0
0.143318774191257 0 0 0 0 0 0
0.150882426050211 0 0 0 0 0 0
0.150917936152835 0 0 0 0 0 0
0.182855985490835 0 0 0 0 0 0
0.257237550749855 0 0 0 0 0 0
0.259735686469467 0 0 0 0 0 0
0.340182837572845 0 0 0 0 0 0
0.437633606761123 0 0 0 0 0 0
0.720513298533433 0 0 0 0 0 0
testPred 0.0268456375838926 0.0280884911757395 0.0283370618941089 0.0285856326124782 0.0315684812329108 0.0328113348247576
0.0165893935955214 0 0 0 0 1 0
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0.0348354106743368 1 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0
0.0379038477472482 0 1 0 0 0 1
0.0497141436738752 0 0 1 0 0 0
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testPred 0.033059905543127 0.0340541884166045 0.0343027591349739 0.0347999005717126 0.0352970420084514 0.0355456127268208
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testPred 0.0382798906288839 0.0395227442207308 0.0420084514044246 0.0430027342779021 0.0432513049962714 0.0449913000248571
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testPred 0.0472284364901814 0.0474770072085508 0.0477255779269202 0.0479741486452896 0.048222719363659 0.052199850857569
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testPred 0.0524484215759384 0.054188416604524 0.0546855580412627 0.0549341287596321 0.056176982351479 0.0584141188168034
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testPred 0.0589112602535421 0.0591598309719115 0.0596569724086503 0.0626398210290828 0.0633855331841909 0.0651255282127765
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testPred 0.0658712403678847 0.0666169525229928 0.0673626646781009 0.0686055182699478 0.0700969425801641 0.0705940840169028
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testPred 0.0710912254536416 0.0730797912005966 0.0743226447924435 0.0750683569475516 0.0753169276659209 0.0755654983842903
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testPred 0.0763112105393985 0.0778026348496147 0.0785483470047228 0.0795426298782003 0.0797912005965697 0.0802883420333085
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testPred 0.081282624906786 0.0842654735272185 0.085259756400696 0.0855083271190654 0.0862540392741735 0.0869997514292816
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testPred 0.0874968928660204 0.0897340293313448 0.0904797414864529 0.0907283122048223 0.0909768829231916 0.0914740243599304
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testPred 0.0922197365150385 0.0924683072334079 0.0934625901068854 0.0944568729803629 0.0954511558538404 0.0959482972905792
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testPred 0.097191150882426 0.0996768580661198 0.0999254287844892 0.100422570221228 0.101416853094705 0.101665423813075 0.104648272433507
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testPred 0.105145413870246 0.105642555306985 0.106885408898832 0.10738255033557 0.107879691772309 0.108625403927417 0.108873974645787
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testPred 0.109371116082525 0.109619686800895 0.11135968182948 0.11160825254785 0.112353964702958 0.115088242605021 0.118816803380562
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testPred 0.120059656972409 0.121053939845886 0.122545364156102 0.125776783494904 0.130748197862292 0.132985334327616 0.135222470792941
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testPred 0.13547104151131 0.136216753666418 0.137708177976634 0.138205319413373 0.138453890131742 0.140193885160328 0.141933880188914
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testPred 0.144916728809346 0.149639572458364 0.156350981854338 0.156599552572707 0.157345264727815 0.157842406164554 0.158588118319662
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0.340182837572845 0 0 0 0 0 0 0
0.437633606761123 0 0 0 0 0 0 0
0.720513298533433 0 0 0 0 0 0 0
testPred 0.158836689038031 0.162813820531941 0.163808103405419 0.165548098434004 0.166045239870743 0.169028088491176 0.177728063634104
0.0165893935955214 0 0 0 0 0 0 0
0.0230993217570399 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0 0
0.0379038477472482 0 0 0 0 0 0 0
0.0497141436738752 0 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0 0
0.122416793094877 0 0 0 1 1 0 0
0.143318774191257 0 1 1 0 0 0 0
0.150882426050211 0 0 0 0 0 0 0
0.150917936152835 1 0 0 0 0 0 0
0.182855985490835 0 0 0 0 0 1 1
0.257237550749855 0 0 0 0 0 0 0
0.259735686469467 0 0 0 0 0 0 0
0.340182837572845 0 0 0 0 0 0 0
0.437633606761123 0 0 0 0 0 0 0
0.720513298533433 0 0 0 0 0 0 0
testPred 0.178722346507581 0.181456624409645 0.185185185185185 0.190405170270942 0.193885160328113 0.19512801391996 0.201342281879195
0.0165893935955214 0 0 0 0 0 0 0
0.0230993217570399 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0 0
0.0379038477472482 0 0 0 0 0 0 0
0.0497141436738752 0 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 1 0
0.0966940094456873 0 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0 0
0.122416793094877 0 0 0 0 0 0 1
0.143318774191257 0 0 0 0 0 0 0
0.150882426050211 0 0 0 0 0 0 0
0.150917936152835 0 0 0 0 0 0 0
0.182855985490835 1 1 0 0 0 0 0
0.257237550749855 0 0 1 0 1 0 0
0.259735686469467 0 0 0 1 0 0 0
0.340182837572845 0 0 0 0 0 0 0
0.437633606761123 0 0 0 0 0 0 0
0.720513298533433 0 0 0 0 0 0 0
testPred 0.204573701217997 0.206810837683321 0.217499378573204 0.219487944320159 0.228436490181457 0.238130748197862 0.241362167536664
0.0165893935955214 0 0 0 0 0 0 0
0.0230993217570399 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0 0
0.0379038477472482 0 0 0 0 0 0 0
0.0497141436738752 0 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0 0
0.122416793094877 1 0 0 0 0 0 0
0.143318774191257 0 0 0 0 0 0 0
0.150882426050211 0 0 0 0 0 0 0
0.150917936152835 0 0 1 0 0 0 0
0.182855985490835 0 1 0 0 0 0 1
0.257237550749855 0 0 0 1 1 0 0
0.259735686469467 0 0 0 0 0 1 0
0.340182837572845 0 0 0 0 0 0 0
0.437633606761123 0 0 0 0 0 0 0
0.720513298533433 0 0 0 0 0 0 0
testPred 0.25478498632861 0.295550584141188 0.341039025602784 0.353716132239622 0.380064628386776 0.393736017897092 0.450658712403679
0.0165893935955214 0 0 0 0 0 0 0
0.0230993217570399 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0 0
0.0379038477472482 0 0 0 0 0 0 0
0.0497141436738752 0 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0 0
0.122416793094877 0 0 0 0 0 0 0
0.143318774191257 0 0 0 0 0 0 0
0.150882426050211 0 0 0 0 0 0 0
0.150917936152835 0 0 0 0 0 0 0
0.182855985490835 0 1 0 0 0 0 0
0.257237550749855 0 0 0 0 0 0 0
0.259735686469467 0 0 0 0 1 0 0
0.340182837572845 1 0 1 0 0 1 0
0.437633606761123 0 0 0 1 0 0 0
0.720513298533433 0 0 0 0 0 0 1
testPred 0.502361421824509 0.512055679840915 0.517027094208302 0.564504101416853 0.573949788714889 0.703455132985334 0.779517772806363 1
0.0165893935955214 0 0 0 0 0 0 0 0
0.0230993217570399 0 0 0 0 0 0 0 0
0.0348354106743368 0 0 0 0 0 0 0 0
0.0366819360107951 0 0 0 0 0 0 0 0
0.0379038477472482 0 0 0 0 0 0 0 0
0.0497141436738752 0 0 0 0 0 0 0 0
0.0528551736605428 0 0 0 0 0 0 0 0
0.0643521970889607 0 0 0 0 0 0 0 0
0.068641028372572 0 0 0 0 0 0 0 0
0.0687298036291325 0 0 0 0 0 0 0 0
0.0884911757394979 0 0 0 0 0 0 0 0
0.0948659789543458 0 0 0 0 0 0 0 0
0.0966940094456873 0 0 0 0 0 0 0 0
0.1221014878733 0 0 0 0 0 0 0 0
0.122416793094877 0 0 0 0 0 0 0 0
0.143318774191257 0 0 0 0 0 0 0 0
0.150882426050211 0 0 0 0 0 0 0 0
0.150917936152835 0 0 0 0 0 0 0 0
0.182855985490835 0 0 0 0 0 0 0 0
0.257237550749855 0 0 1 0 0 0 0 0
0.259735686469467 1 0 0 0 0 0 0 0
0.340182837572845 0 0 0 0 0 0 0 0
0.437633606761123 0 0 0 1 1 0 0 0
0.720513298533433 0 1 0 0 0 1 1 1
library(e1071)
library(caret)
co_result <- confusionMatrix(result)
Error in !all.equal(nrow(data), ncol(data)) : invalid argument type
>>>>>>> Stashed changes
<<<<<<< Updated upstream
precision70ctree=((( 1339/2682)+( 913/2738)+(1349/3081))/3)*100
sensitivity70ctree=((0.8091+0.5581+0.8384)/3)*100
specificity70ctree=((0.8265+0.8388+0.9362)/3)*100
=======
precision70ctree=((( 1341/2558)+( 811/2777)+(1362/2974))/3)*100
sensitivity70ctree=((0.8268+0.5104+0.8598)/3)*100
specificity70ctree=((0.8062+0.8662+ 0.9262)/3)*100
>>>>>>> Stashed changes
table=matrix(c(acc90ctree,acc80ctree,acc70ctree
,precision90ctree,precision80ctree,precision70ctree,
sensitivity90ctree,sensitivity80ctree,sensitivity70ctree,
specificity90ctree,specificity80ctree,specificity70ctree
<<<<<<< Updated upstream
),ncol=3,byrow=TRUE)
colnames(table)=c('90% train,10% test','80% train,20% test','70% train,30% test')
rownames(table)=c("accuracy","precision","sensitivity","specificity")
finaltable=as.table(table)
finaltable
The accuracy measurement is not always enough to determine the best split of dataset to create the model starting with the highest accuracy(70% training,30% test) we can see that the sensitivity is very close to the accuracy so it’s balanced and we might consider it good choice for the model
Then we have (90% training,10% test) It has a Precision=42.3% which means our model correctly predict more than 43% out of all the predictions made, but it has the least accuracy percentage (73.06%) so it’s not the best option.
Lastly (80% training,20% test) precision=42.5% and it also has the highest Precision among other split and high accuracy (73.3%) so it’s might be a good choice for the model
======= ),ncol=3,byrow=TRUE)Error in matrix(c(acc90ctree, acc80ctree, acc70ctree, precision90ctree, :
object 'acc90ctree' not found
The accuracy measurement is not always enough to determine the best split of dataset to create the model. starting with the highest accuracy(70% training,30% test) we can see that the sensitivity is very close to the accuracy so it’s balanced and we might consider it good choice for the model
Then we have (90% training,10% test) It has a Precision=43.3% which means our model correctly predict more than 43% out of all the predictions made. and that percentage is higher than all other the splits which is a good thing.
Lastly (80% training,20% test) If classifier predicts positive, you can’t trust it since the precision=41.9% and it also has the lowest Precision among other split(41.9%). so it’s not really a good choice for the model
>>>>>>> Stashed changes#C5.0 method In decision tree algorithms, such as the C5.0 algorithm, the Gain Ratio is used to measure the effectiveness of splitting nodes during the tree-building process. Information gain is one of the limitations of other splitting criteria, which is why the Gain Ratio was developed. Using the Gain Ratio at each node of the decision tree, the C5.0 algorithm, an extension of C4.5, determines the best attribute to split on. Gain Ratios are particularly useful when dealing with attributes with different categories or levels # C5.0 tree: 80% traning set, and 20% testing set
<<<<<<< Updated upstreamlibrary(remotes)
library("C50")
library(printr)
set.seed(15687)
=======
library(remotes)
library("C50")
library(printr)
Registered S3 method overwritten by 'printr':
method from
knit_print.data.frame rmarkdown
set.seed(15687)
>>>>>>> Stashed changes
ind <- sample(2, nrow(dataset), replace=TRUE, prob=c(0.8, 0.2))
trainData <- dataset[ind==1,]
testData <- dataset[ind==2,]
myFormula <- Global_Sales ~ NA_Sales +EU_Sales+Other_Sales+Genre+Platform
<<<<<<< Updated upstream
model <- C5.0(myFormula, data=trainData)
plot(model)
pred <- predict(object=model, newdata=testData, type="class")
result<-table(pred, testData$Global_Sales)
co_result <- confusionMatrix(result)
print(co_result)
acc80C50 <- co_result$overall["Accuracy"]
acc80C50=acc80C50*100
=======
model <- C5.0(myFormula, data=trainData)
Error: C5.0 models require a factor outcome
>>>>>>> Stashed changes
<<<<<<< Updated upstream
precision80C50=((( 946/1728)+( 532/1915)+(936/2051))/3)*100
sensitivity80C50=((0.8584+0.4890+0.8587)/3)*100
specificity80C50=((0.7934+0.8736+0.9365)/3)*100
=======
precision80C50=((( 912/1700)+( 541/1874)+(894/1982))/3)*100
sensitivity80C50=((0.8452+0.5128+0.8386)/3)*100
specificity80C50=((0.8015+0.8695+0.9288)/3)*100
>>>>>>> Stashed changes
library(remotes)
library("C50")
library(printr)
set.seed(15687)
ind <- sample(2, nrow(dataset), replace=TRUE, prob=c(0.7, 0.3))
trainData <- dataset[ind==1,]
testData <- dataset[ind==2,]
myFormula <- Global_Sales ~ NA_Sales +EU_Sales+Other_Sales+Genre+Platform
<<<<<<< Updated upstream
model <- C5.0(myFormula, data=trainData)
plot(model)
results <- predict(object=model, newdata=testData, type="class")
result<-table(results, testData$Global_Sales)
co_result <- confusionMatrix(result)
print(co_result)
acc70C50 <- co_result$overall["Accuracy"]
acc70C50=acc70C50*100
=======
model <- C5.0(myFormula, data=trainData)
Error: C5.0 models require a factor outcome
>>>>>>> Stashed changes
<<<<<<< Updated upstream
precision70C50=((( 1454/2551)+(792/2858)+(1346/3083))/3)*100
sensitivity70C50=(( 0.8785+0.4841+ 0.8365)/3)*100
specificity70C50=((0.7861+0.8756+0.9368)/3)*100
=======
precision70C50=((( 1331/2594)+( 828/2765)+(1368/2963))/3)*100
sensitivity70C50=(( 0.8206+0.5211+0.8636)/3)*100
specificity70C50=((0.8175+0.8624+0.9228)/3)*100
>>>>>>> Stashed changes
library(remotes)
library("C50")
library(printr)
set.seed(15687)
ind <- sample(2, nrow(dataset), replace=TRUE, prob=c(0.9, 0.1))
trainData <- dataset[ind==1,]
testData <- dataset[ind==2,]
myFormula <- Global_Sales ~ NA_Sales +EU_Sales+Other_Sales+Genre+Platform
<<<<<<< Updated upstream
model <- C5.0(myFormula, data=trainData)
plot(model)
results <- predict(object=model, newdata=testData, type="class")
result<-table(results, testData$Global_Sales)
co_result <- confusionMatrix(result)
print(co_result)
acc90C50 <- co_result$overall["Accuracy"]
acc90C50=acc90C50*100
=======
model <- C5.0(myFormula, data=trainData)
Error: C5.0 models require a factor outcome
>>>>>>> Stashed changes
<<<<<<< Updated upstream
precision90C50=(((450/853)+(268/932)+(460/1004))/3)*100
sensitivity90C50=(( 0.8227+0.4926+0.8846)/3)*100
specificity90C50=(( 0.8017+0.8735+0.9203)/3)*100
=======
precision90C50=((( 433/828)+( 273/870)+(412/988))/3)*100
sensitivity90C50=(( 0.8109+ 0.5122+ 0.8224)/3)*100
specificity90C50=(( 0.8008+0.8406+0.9260)/3)*100
>>>>>>> Stashed changes
table=matrix(c(acc90C50,acc80C50,acc70C50
,precision90C50,precision80C50,precision70C50,
sensitivity90C50,sensitivity80C50,sensitivity70C50,
specificity90C50,specificity80C50,specificity70C50
<<<<<<< Updated upstream
),ncol=3,byrow=TRUE)
colnames(table)=c('90% train,10% test','80% train,20% test','70% train,30% test')
rownames(table)=c("accuracy","precision","sensitivity","specificity")
finaltable=as.table(table)
finaltable
we can clearly see that results are very close to that information again results. The split that had the highest accuracy is (80% training,20% test) and also had the highest Precision among all the split and has a sensitivity very close to the accuracy which make it balanced and a good choice for the model.
At the same time (70% training,30% test) split had very close accuracy to the previous split but it also had less sensitivity result which is 73.2% so it doesn’t really make it a good choice.
Lastly (90% training,10% test) It has the lowest accuracy (73.12%) and Precision(42.4%) So it’s the least split option to go for the model
======= ),ncol=3,byrow=TRUE)Error in matrix(c(acc90C50, acc80C50, acc70C50, precision90C50, precision80C50, :
object 'acc90C50' not found
we can clearly see that results are very close to that information again result. The split that had the highest accuracy is (70% training,30% test) and also had the highest sensitivity among all the split and very close to the accuracy which make it balanced and a good choice for the model.
At the same time (80% training,20% test) split had very close accuracy to the previous split but it also had less sensitivity result which is 73.2% so it doesn’t really make it a good choice.
Lastly (90% training,10% test) It has the lowest accuracy (71.3%) and Precision(41.7%) So it’s the least split option to go for the model
>>>>>>> Stashed changes#CART
CART (Classification and Regression Trees) in R uses the Gini index to determine whether a randomly chosen element would be incorrectly classified. In the Gini index, we assess how likely it is for an element to be misclassified if it was randomly chosen. Generally, the lower the Gini index, the better the split.
#CART tree: 90% training and 10% testing
set.seed(15687)
sample<-sample.int(n=nrow(dataset),size=floor(0.9*nrow(dataset)), replace=F)
trainCart90<-dataset[sample,]
testCart10<-dataset[-sample,]
trainCart90<-trainCart90[,c(7:10,3,5)]
testCart10<-testCart10[,c(7:10,3,5)]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
library(rpart)
library(rpart.plot)
Global_Sales.test=testCart10$Global_Sales
<<<<<<< Updated upstream
fit.tree = rpart(Global_Sales~ ., data=trainCart90, method = "class", cp=4.6816e-04)
fit.tree
rpart.plot(fit.tree)
fit.tree$variable.importance
pred.tree = predict(fit.tree, testCart10, type = "class")
table(pred.tree,Global_Sales.test)
printcp(fit.tree)
=======
fit.tree = rpart(Global_Sales~ ., data=trainCart90, method = "class", cp=0)
>>>>>>> Stashed changes
<<<<<<< Updated upstream
#Accuracy/precision/sensitivity of model is given by
accuracy90=((477+ 246 + 456)/nrow(testCart10))*100
precision90=(((477/833)+(246/955)+(456/996))/3)*100
sensitivity90=(((477/547)+(246/535)+(456/523))/3)*100
specificity90=((((833)/1058)+((955)/1070)+((996)/1082))/3)*100
=======
#Accuracy/precision/sensitivity of model is given by
accuracy90=((510+ 150 + 447)/nrow(testCart10))*100
precision90=(((510/851)+(150/211)+(447/507))/3)*100
sensitivity90=(((510/512)+(150/510)+(447/547))/3)*100
specificity90=((((150+54+53+463)/1057)+((510+0+30+463)/1059)+((510+307+2+150)/1022))/3)*100
>>>>>>> Stashed changes
#CART tree: 80% training and 20% testing
set.seed(15687)
sample<-sample.int(n=nrow(dataset),size=floor(0.8*nrow(dataset)), replace=F)
trainCart80<-dataset[sample,]
testCart20<-dataset[-sample,]
trainCart80<-trainCart80[,c(7:10,3,5)]
testCart20<-testCart20[,c(7:10,3,5)]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
library(rpart)
library(rpart.plot)
Global_Sales.test=testCart20$Global_Sales
<<<<<<< Updated upstream
fit.tree = rpart(Global_Sales~ ., data=trainCart80, method = "class", cp=4.6816e-04)
=======
fit.tree = rpart(Global_Sales~ ., data=trainCart80, method = "class", cp=0)
>>>>>>> Stashed changes
fit.tree
rpart.plot(fit.tree)
fit.tree$variable.importance
pred.tree = predict(fit.tree, testCart20, type = "class")
table(pred.tree,Global_Sales.test)
printcp(fit.tree)
<<<<<<< Updated upstream
#Accuracy/precision/sensitivity of model is given by
accuracy80=((873+ 541 + 941)/nrow(testCart20))*100
precision80=(((873/1748)+(541/1848)+(941/1968))/3)*100
sensitivity80=(((873/1073)+(541/562)+(941/1087))/3)*100
specificity80=((((1073)/2136)+((562)/2160)+((1087)/2122))/3)*100
=======
#Accuracy/precision/sensitivity of model is given by
accuracy80=((1033+ 321 + 875)/nrow(testCart20))*100
precision80=(((1033/1688)+(321/443)+(875/1007))/3)*100
sensitivity80=(((1033/1037)+(321/1036)+(875/1065))/3)*100
specificity80=((((323+112+113+887)/2101)+((1033+1+66+887)/2102)+((1033+600+3+323)/2073))/3)*100
>>>>>>> Stashed changes
#CART tree: 70% training and 30% testing
set.seed(15687)
sample<-sample.int(n=nrow(dataset),size=floor(0.7*nrow(dataset)), replace=F)
trainCart70<-dataset[sample,]
testCart30<-dataset[-sample,]
trainCart70<-trainCart70[,c(7:10,3,5)]
testCart30<-testCart30[,c(7:10,3,5)]
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
library(rpart)
library(rpart.plot)
Global_Sales.test=testCart30$Global_Sales
<<<<<<< Updated upstream
fit.tree = rpart(Global_Sales~ ., data=trainCart70, method = "class", cp=4.6816e-04)
=======
fit.tree = rpart(Global_Sales~ ., data=trainCart70, method = "class", cp=0)
>>>>>>> Stashed changes
fit.tree
rpart.plot(fit.tree)
fit.tree$variable.importance
pred.tree = predict(fit.tree, testCart30, type = "class")
table(pred.tree,Global_Sales.test)
printcp(fit.tree)
<<<<<<< Updated upstream
#Accuracy/precision/sensitivity of model is given by
accuracy70=((1364+ 787 + 1410)/nrow(testCart30))*100
precision70=(((1364/2572)+(787/2849)+(1410/2953))/3)*100
sensitivity70=(((1364/1610)+(321/1570)+(875/1633))/3)*100
specificity70=((((1610)/3203)+((1570)/3243)+((1633)/3180))/3)*100
=======
#Accuracy/precision/sensitivity of model is given by
accuracy70=((1569+ 466 + 1341)/nrow(testCart30))*100
precision70=(((1569/2535)+(466/636)+(1341/1536))/3)*100
sensitivity70=(((1569/1575)+(321/1529)+(875/1603))/3)*100
specificity70=((((466+166+193+1341)/3132)+((1569+2+96+1341)/3178)+((1033+600+3+323)/3104))/3)*100
>>>>>>> Stashed changes
#CART tree result Evaluation and analysis:
table=matrix(c(accuracy90,accuracy80,accuracy70
,precision90,precision80,precision70,
sensitivity90,sensitivity80,sensitivity70,
specificity90,specificity80,specificity70
),ncol=3,byrow=TRUE)
colnames(table)=c('90% train,10% test','80% train,20% test','70% train,30% test')
rownames(table)=c("accuracy","precision","sensitivity","specificity")
finaltable=as.table(table)
finaltable
First we used the above partitioning for the training set/testing set;An evaluation of a model’s based on one dataset may not be accurate. Different sets of training and test data help to obtain a more sound evaluation of the model.
From the result above we can see that the 70% training set, 30% testing set gave the best accuracy but we don’t only judge the accuracy, because if we do that we may use an imbalanced set, let’s compare the sensitivity/specificity too, we can notice that the sensitivity of the <<<<<<< Updated upstream 70% training set, 30% testing set is pretty low(53%) so it has imbalance issues so even if the accuracy is high we won’t consider it as the best model. We will see the results of the second best accuracy set which is:90% training set, 10% testing set, the sensitivity is pretty high(73.5%, and 87% respectively) we will compare it to 80% and 20% who has sensitivity/specificity of(88%, and 42.5% respectively), since the 90% training set, 10% testing set has the best specificity between the two we will consider it as the best training/testing set.
======= 70% training set, 30% testing set is pretty low(58%) so it has imbalance issues so even if the accuracy is high we won’t consider it as the best model. We will see the results of the second best accuracy set which is:80% training set, 20% testing set, the sensitivity/specificity s pretty high(71%, and 86% respectively), even higher than the 90% training set, 10% testing set(70%, and 86% respectively), so it’s pretty balanced. the ratio of true positives to the total number of predicted positives (precision)=73.5% which means only 26.5% is classified as FP!.I will start explaining the results of the best partition(80% training set, 20% testing set) 1-We can notice from the tree that it peridicted for the 3 levels of global_Sales a total of: [0,000967]:52% [0.000967,0.00387]:16% [0.00387,1]:32% We can notice that a range of Global_sales with [0,000967] is the norm for most of the games, while being in the middle is not common, most of our dataset fells on the lowest or highest of predicted Global_sales.
1-if NA_Sales=[0.00386,1] and EU_Sales=[0.00207,1] then Global_Sales=[0.00387,1], Games has a high Global_Sales because their high EU_Sales and NA_Sales range of sales , so video games company need to focus on those two regions if they want to improve their Global sales.
you can also acheive high Global_Sales by focusing on keeping your sales on these regions as follow:
2-if NA_Sales=[0.00386,1] and EU_Sales=[0,0.00207] and Other_Sales=[0.00189,1] then you will have a high sales, You can keep the NA_Sales and Other_Sales region high while ignoring EU_Sales to also acheive high Global_Sales.
3-if NA_Sales=[0,000482),[0.000482,00386] and other_Sales=[0.00189,1], and NA_Sales=[0,000482) and EU_Sales=[0,0.00207) then you will have high Gloabl_Sales.
You can also have low NA_Sales and EU_Sales, while having Other_Sales high to achieve high Global_Sales.
>>>>>>> Stashed changesClustering is a technique used to group similar data points together based on their inherent characteristics or similarities. So our goal of clustering is to identify patterns, structures, or relationships within a dataset without any prior knowledge of the groups or classes that may exist.
before starting the clustring process we need to remove the class label since clustring is an unsupervised learning , but before removing the class label We stored it in a varible just in case of further need(We need it to compute Bcubed precision and recall),Then we need to transform each factor coulnm to numeric because it’s essential to convert factor variables to numeric ones due to the algorithmic requirements of clustring(Kmeans) and the characteristics of factor variables.
<<<<<<< Updated upstream# We stored the class label in a varible just in case of further need(We need it to compute Bcubed precision and recall)
classLabel<-dataset$Global_Sales
=======
# We stored the class label in a varible just in case of further need(We need it to compute Bcubed precision and recall)
classLabel<-dataset$Global_Sales
>>>>>>> Stashed changes
# Removing the classLabel before the clustring process
datasetClustering<- dataset[,-10]
# We removed columns that are not relevant to the clustering process and can distort the result
datasetClustering <- dataset[, setdiff(3:9, c(4, 6))]
View(datasetClustering)
##converting factors to numric to apply kmeans method , it's essential to convert factor variables to numeric ones due to the algorithmic requirements of K-means and the characteristics of factor variables.
datasetClustering$Platform <- as.numeric(as.character(datasetClustering$Platform))
datasetClustering$Genre <- as.numeric(as.character(datasetClustering$Genre))
View(datasetClustering)
After preprocessing the data now we will start performing the
clustering technique on the processed dataset.
We chose K-means clustering as our clustring method because it excels in handling large datasets, offering prompt and easily understandable insights. It is beneficial for exploring data, facilitating the quick detection of potential data clusters.
This graph depicts the process of finding the optimal number of clusters for a dataset using the Silhouette method. The x-axis represents the number of clusters (k) considered in the analysis, ranging from 1 to 10. The y-axis shows the average Silhouette width, which is a measure of how similar an object is to its own cluster compared to other clusters.
<<<<<<< Updated upstreamfviz_nbclust(datasetClustering, kmeans, method = "silhouette")+
labs(subtitle = "Silhouette method")
=======
fviz_nbclust(datasetClustering, kmeans, method = "silhouette")+labs(subtitle = "Silhouette method")
The plot shows a peak at k=3, where the average Silhouette score is the highest. This suggests that the data points are, on average, closer to other points in their own cluster and farther from points in other clusters when the data is divided into three clusters. As a result, according to the Silhouette method, k=3 is the optimal number of clusters.
The Elbow Method using Within-Cluster Sum of Squares (WSS) is a technique to determine the optimal number of clusters in K-means clustering. It involves running the clustering algorithm for a range of cluster numbers and calculating the WSS for each. WSS is the sum of squared distances of each point to its cluster centroid. As the number of clusters increases, WSS tends to decrease; the goal is to find the point where increasing the number of clusters does not lead to a significant decrease in WSS. This point, visually resembling an elbow on a plot of WSS against the number of clusters, is considered the optimal number of clusters.
fviz_nbclust(datasetClustering, kmeans, method = "wss") +
geom_vline(xintercept = 4, linetype = 2)+
labs(subtitle = "Elbow method")
<<<<<<< Updated upstream
=======
As shown in the above graph , 4 is the value that resembles an elbow in the plot(The turnning point) wich means it is the optimal value of K the we will use in our clustring process.
In conclusion, we will choose K=4 for our clustering process, as it marks the turning point on the Elbow Method curve, indicating an optimal balance in cluster compactness and separation. Additionally, we will utilize K=3 and K=6, as these values maximize the average silhouette width, with K=3 being the primary maximizer and K=6 the secondary. By selecting these specific K values, we aim to achieve a satisfactory level of precision and recall in our clustering analysis, ensuring both the relevance and completeness of the clustered data.
set.seed(5000)
kmeans.result <- kmeans(datasetClustering, 3)
# print the clusterng result
kmeans.result
<<<<<<< Updated upstream
=======
K-means clustering with 3 clusters of sizes 156, 297, 169
Cluster means:
Platform Genre NA_Sales EU_Sales Other_Sales
1 6.102564 6.192308 0.08192255 0.10287267 0.02656284
2 16.932660 5.996633 0.06168748 0.09816854 0.04852690
3 27.763314 6.905325 0.09602565 0.11282572 0.03891778
Clustering vector:
[1] 3 2 2 3 1 2 1 3 1 2 2 2 2 2 1 2 2 2 1 2 2 1 1 3 2 1 2 2 3 2 3 3 2 2 1 1 2 1 2 1 2 1 1 3 1 2 3 2 2 2 2 2 2 3 2 3 3 1 3 1 2 2 1 3 2 1 2 2 2 3 2 2
[73] 1 1 2 2 2 3 2 1 1 3 2 2 2 2 3 3 3 2 3 3 1 2 3 1 3 2 3 1 3 3 2 2 3 2 3 2 3 1 2 3 1 2 2 1 2 2 2 2 3 2 2 3 2 1 3 3 2 1 1 2 2 1 2 2 3 1 1 3 2 2 3 2
[145] 2 1 3 2 3 2 2 2 2 3 3 2 1 1 3 1 2 1 2 3 1 2 2 2 2 2 3 1 2 3 3 1 2 2 2 2 2 1 2 2 2 2 1 2 1 2 3 3 3 1 3 2 3 3 1 3 1 3 2 1 2 3 1 1 2 3 2 1 1 2 2 3
[217] 1 1 1 3 3 3 2 1 2 3 1 3 2 3 2 1 3 3 1 1 2 2 3 3 2 2 2 2 2 3 3 2 2 2 2 2 1 1 2 1 2 2 1 2 2 3 3 1 3 3 1 2 1 3 3 1 2 3 1 2 2 2 1 2 1 2 2 3 3 2 1 2
[289] 2 1 3 3 2 3 2 2 1 3 3 3 2 3 3 1 2 2 3 2 2 2 1 1 2 3 3 2 2 2 2 2 1 2 2 2 3 1 3 2 3 2 2 1 2 2 1 2 3 1 3 1 3 2 2 2 2 2 2 1 2 2 1 2 3 2 1 1 2 2 2 2
[361] 3 2 3 2 1 3 1 2 2 2 2 1 2 1 2 2 3 1 2 2 1 2 2 2 3 3 3 2 2 2 2 2 3 2 1 2 2 2 2 3 1 2 1 2 2 2 2 2 3 3 2 2 2 2 1 3 2 3 1 1 3 3 2 3 2 3 2 2 3 2 2 2
[433] 2 2 2 2 1 1 2 2 3 2 3 2 2 2 1 3 2 2 3 1 1 1 3 1 2 2 2 2 1 3 2 2 1 1 2 2 2 1 1 1 1 2 3 2 3 2 2 2 1 2 2 3 1 2 2 2 1 2 3 3 2 2 3 1 3 3 1 2 1 3 2 2
[505] 2 3 2 1 2 2 2 3 3 2 2 2 3 1 2 1 2 2 2 2 3 3 3 3 3 1 2 2 2 2 3 2 1 1 2 2 3 1 2 2 1 3 2 3 3 1 1 3 3 3 2 3 2 2 3 1 1 3 3 3 1 1 3 2 2 2 1 2 3 1 1 3
[577] 2 1 1 2 3 1 1 2 3 2 2 3 3 2 2 1 1 3 2 1 1 1 2 3 2 1 1 1 3 2 2 3 3 3 1 1 1 2 3 3 1 1 1 3 3 2
Within cluster sum of squares by cluster:
[1] 2743.622 4885.759 3221.026
(between_SS / total_SS = 77.9 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss" "size" "iter" "ifault"
>>>>>>> Stashed changes
# visualize clustering
library(factoextra)
fviz_cluster(kmeans.result, data = datasetClustering)
<<<<<<< Updated upstream
=======
#average silhouette for cluster k=3
library(cluster)
avg_sil <- silhouette(kmeans.result$cluster,dist(datasetClustering))
fviz_silhouette(avg_sil)
<<<<<<< Updated upstream
=======
#Within-cluster sum of squares wss
wss <- kmeans.result$tot.withinss
print(wss)
<<<<<<< Updated upstream
=======
[1] 10850.41
>>>>>>> Stashed changes
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
#BCubed
kmeans_cluster <- c(kmeans.result$cluster)
ground_truth <- c(classLabel)
data <- data.frame(cluster = kmeans_cluster, label = ground_truth)
# Function to calculate BCubed precision and recall
bcubed <- function(data) {
n <- nrow(data)
total_precesion <- 0
total_recall <- 0
for (i in 1:n) {
cluster <- data$cluster[i]
label <- data$label[i]
# Count the number of items from the same category within the same cluster
intersection <- sum(data$label[data$cluster == cluster] == label)
# Count the total number of items in the same cluster
total_same_cluster <- sum(data$cluster == cluster)
# Count the total number of items with the same category
total_same_category <- sum(data$label == label)
# Calculate precision and recall for the current item and add them to the sums
total_precesion <- total_precesion + intersection /total_same_cluster
total_recall <- total_recall + intersection / total_same_category
}
# Calculate average precision and recall
precision <- total_precesion / n
recall <- total_recall / n
return(list(precision = precision, recall = recall))
}
# Calculate BCubed precision and recall
metrics <- bcubed(data)
# Extract precision and recall from the metrics
precision <- metrics$precision
recall <- metrics$recall
# Print the results
<<<<<<< Updated upstream
cat("BCubed Precision:", precision, "\n")
cat("BCubed Recall:", recall, "\n")
=======
cat("BCubed Precision:", precision, "\n")
BCubed Precision: 0.004823151
cat("BCubed Recall:", recall, "\n")
BCubed Recall: 1
>>>>>>> Stashed changes
As the graph of K=3 illustrated , there is a noticeable overlapping between the clusters that effect the cluster performance duo to the similarity between clusters and wide distance in the cluster itself as <<<<<<< Updated upstream the high value of wss indicate (78.5 %) ,he recall value is 1 wich means that all items that must belong to their specified cluster has been correctly identified , the value of precision 0.004823151 is low which can be duo to the presence of outliers ,the value of average silhouette width is 0.55 which is relatively good for the clustering process, Overall, the plot suggests that dividing the data into 3 clusters seems appropriate because the average Silhouette score is high.
======= the high value of wss indicate (78.5 %) ,the recall value is high which is 1 , the value of precision 0.004823151 is low which can be duo to the presence of outliers ,the value of average silhouette width is 0.55 which is relatively good for the clustering process, Overall, the plot suggests that dividing the data into 3 clusters seems appropriate because the average Silhouette score is high. >>>>>>> Stashed changesset.seed(5000)
kmeans.result <- kmeans(datasetClustering, 4)
# print the clusterng result
kmeans.result
<<<<<<< Updated upstream
=======
K-means clustering with 4 clusters of sizes 112, 194, 168, 148
Cluster means:
Platform Genre NA_Sales EU_Sales Other_Sales
1 16.803571 1.821429 0.07981369 0.1107129 0.05441614
2 16.788660 8.567010 0.05017442 0.0877049 0.04312926
3 27.797619 6.880952 0.09659724 0.1134973 0.03914943
4 5.837838 6.027027 0.08397385 0.1066865 0.02766627
Clustering vector:
[1] 3 2 2 3 4 2 4 2 4 2 1 1 2 2 4 2 2 1 2 2 2 4 4 3 2 4 2 1 3 2 3 3 2 2 2 4 1 4 2 4 2 4 4 3 4 2 3 2 2 2 1 2 2 3 2 3 3 2 3 4 1 2 4 3 2 4 2 2 2 3 2 1
[73] 4 4 2 2 2 3 2 4 4 3 2 1 1 2 3 3 3 1 3 3 4 2 3 4 3 2 3 4 3 3 2 2 3 1 3 2 3 4 2 3 4 2 2 4 2 1 2 1 3 1 2 3 2 4 3 3 2 4 4 1 2 4 2 2 3 4 4 3 2 2 3 2
[145] 1 4 3 2 3 2 1 2 2 3 3 1 4 4 3 4 2 4 2 3 4 1 2 2 1 2 3 4 2 3 3 4 2 1 2 2 1 4 2 2 1 2 4 2 4 2 3 3 3 4 3 2 3 3 4 3 4 3 2 4 2 3 4 4 2 3 2 4 4 2 2 3
[217] 4 4 4 3 3 3 1 4 1 3 4 3 1 3 2 4 3 3 4 4 2 2 3 3 2 2 1 2 2 3 3 2 1 2 1 2 4 4 2 4 1 2 4 1 2 3 3 1 3 3 4 1 2 3 3 2 1 3 4 2 1 2 4 2 4 2 1 3 3 2 4 2
[289] 2 4 3 3 1 3 1 2 4 3 3 3 1 3 3 4 2 1 3 2 1 2 4 4 2 3 3 2 2 2 1 1 4 1 1 2 3 4 3 1 3 2 2 4 1 2 4 2 3 4 3 4 3 1 1 1 1 1 2 4 2 1 2 1 3 2 4 4 2 1 1 2
[361] 3 1 3 1 4 3 4 2 2 2 2 4 2 4 1 2 3 4 2 2 4 1 2 1 3 3 3 2 2 2 1 1 3 2 4 1 1 1 1 3 4 2 4 2 2 1 2 2 3 3 1 1 1 2 4 3 1 3 4 4 3 3 1 3 1 3 2 1 3 2 2 2
[433] 2 1 1 2 4 4 2 1 3 2 3 2 2 1 4 3 1 2 3 4 4 4 3 4 2 2 2 1 4 3 2 2 4 4 2 1 2 2 4 4 4 2 3 1 3 1 1 2 4 1 1 3 4 1 2 1 4 1 3 3 1 2 3 4 3 3 4 2 4 3 1 1
[505] 2 3 1 4 2 2 1 3 3 1 1 2 3 4 2 4 1 1 2 2 3 3 3 3 3 4 1 1 2 2 3 1 4 4 2 2 3 4 2 1 4 3 2 3 3 4 4 3 3 3 2 3 2 2 3 4 4 3 3 3 4 4 3 1 2 2 4 2 3 4 4 3
[577] 2 4 4 1 3 4 4 2 3 1 2 3 3 2 2 4 4 3 2 4 4 4 1 3 1 4 4 4 3 1 1 3 3 3 4 4 4 2 3 3 4 4 4 3 3 1
Within cluster sum of squares by cluster:
[1] 597.8142 1548.3406 3170.7230 2378.9557
(between_SS / total_SS = 84.3 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss" "size" "iter" "ifault"
>>>>>>> Stashed changes
# visualize clustering
library(factoextra)
fviz_cluster(kmeans.result, data = datasetClustering)
<<<<<<< Updated upstream
=======
#average silhouette for cluster k=4
library(cluster)
avg_sil <- silhouette(kmeans.result$cluster,dist(datasetClustering))
fviz_silhouette(avg_sil)
<<<<<<< Updated upstream
=======
#Within-cluster sum of squares wss
wss <- kmeans.result$tot.withinss
print(wss)
<<<<<<< Updated upstream
=======
[1] 7695.834
>>>>>>> Stashed changes
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
#BCubed
kmeans_cluster <- c(kmeans.result$cluster)
ground_truth <- c(classLabel)
data <- data.frame(cluster = kmeans_cluster, label = ground_truth)
# Function to calculate BCubed precision and recall
bcubed <- function(data) {
n <- nrow(data)
total_precesion <- 0
total_recall <- 0
for (i in 1:n) {
cluster <- data$cluster[i]
label <- data$label[i]
# Count the number of items from the same category within the same cluster
intersection <- sum(data$label[data$cluster == cluster] == label)
# Count the total number of items in the same cluster
total_same_cluster <- sum(data$cluster == cluster)
# Count the total number of items with the same category
total_same_category <- sum(data$label == label)
# Calculate precision and recall for the current item and add them to the sums
total_precesion <- total_precesion + intersection /total_same_cluster
total_recall <- total_recall + intersection / total_same_category
}
# Calculate average precision and recall
precision <- total_precesion / n
recall <- total_recall / n
return(list(precision = precision, recall = recall))
}
# Calculate BCubed precision and recall
metrics <- bcubed(data)
# Extract precision and recall from the metrics
precision <- metrics$precision
recall <- metrics$recall
# Print the results
<<<<<<< Updated upstream
cat("BCubed Precision:", precision, "\n")
cat("BCubed Recall:", recall, "\n")
=======
cat("BCubed Precision:", precision, "\n")
BCubed Precision: 0.006430868
cat("BCubed Recall:", recall, "\n")
BCubed Recall: 1
>>>>>>> Stashed changes
As the graph of K=4 illustrated , there is a noticeable overlapping between the clusters that effect the cluster performance duo to the similarity between clusters and some wide distance in the cluster itself <<<<<<< Updated upstream as the high value of wss indicate(85.3 %) ,the recall value is 1 wich means that all items that must belong to their specified cluster has been correctly identified , the value of precision 0.006430868 is low which can be duo to the presence of outliers ,the value of average silhouette width is 0.54 which is relatively good for the clustering process but it is lower than K=3, Overall, the plot suggests that dividing the data into 4 clusters seems appropriate but it would better if choose k=3 because it has highest average silhouette value .
======= as the high value of wss indicate(85.3 %) ,the recall value is high which is 1 , the value of precision 0.006430868 is low which can be duo to the presence of outliers ,the value of average silhouette width is 0.54 which is relatively good for the clustering process, Overall, the plot suggests that dividing the data into 4 clusters seems appropriate but it would better if choose k=3 because it has highe average silhouette widthand lower wss value . >>>>>>> Stashed changesset.seed(5000)
kmeans.result <- kmeans(datasetClustering, 6)
# print the clusterng result
kmeans.result
<<<<<<< Updated upstream
=======
K-means clustering with 6 clusters of sizes 110, 178, 60, 85, 81, 108
Cluster means:
Platform Genre NA_Sales EU_Sales Other_Sales
1 16.890909 1.763636 0.07951732 0.11181536 0.05522491
2 17.258427 8.471910 0.04728142 0.09372601 0.04643203
3 27.350000 2.350000 0.09370128 0.11224120 0.03584043
4 5.058824 4.035294 0.09299296 0.09837413 0.02857143
5 7.938272 8.802469 0.07449013 0.09703052 0.02207506
6 28.046296 9.398148 0.09820610 0.11419513 0.04098777
Clustering vector:
[1] 3 2 2 3 5 2 4 2 5 2 1 1 2 2 5 2 2 1 5 2 2 4 4 6 2 5 2 1 6 2 3 6 2 2 5 5 1 5 2 4 2 5 5 3 5 2 6 2 2 2 1 2 2 3 2 6 6 5 6 5 1 2 5 6 2 5 2 2 2 6 2 1
[73] 5 5 2 2 2 6 2 5 4 6 2 1 1 2 6 3 3 1 6 6 4 2 3 4 6 2 3 5 6 6 2 2 3 1 3 2 6 5 2 6 4 2 2 5 2 1 2 1 6 1 2 6 5 4 6 3 2 5 4 1 2 4 2 2 6 4 4 6 2 2 3 2
[145] 1 4 3 2 6 2 1 2 2 3 6 1 5 4 3 5 2 5 2 6 5 1 2 2 1 2 3 5 2 6 3 4 2 1 2 2 1 4 2 2 1 2 4 2 4 2 6 6 6 4 3 5 6 6 4 3 4 6 2 4 2 6 5 5 2 6 2 5 4 5 5 6
[217] 5 4 5 6 6 3 1 4 1 3 4 6 1 3 2 4 6 3 4 4 2 2 3 6 2 2 1 2 2 6 6 2 1 2 1 2 4 5 2 5 1 2 4 1 2 6 3 1 6 6 4 1 5 6 3 5 1 6 4 2 1 2 4 2 4 2 1 3 6 2 5 2
[289] 2 4 3 6 1 3 1 2 5 6 6 3 1 6 3 4 5 1 6 5 1 2 5 4 2 3 6 5 2 2 1 1 4 1 1 2 6 5 3 1 3 2 2 4 1 2 4 2 3 5 6 5 3 1 1 1 1 1 2 4 2 1 5 1 6 2 5 5 2 1 1 2
[361] 6 1 6 1 5 6 4 2 2 5 2 4 2 4 1 2 6 4 2 2 5 1 2 1 6 6 6 2 2 2 1 1 3 2 4 1 1 1 1 6 4 2 4 2 2 1 2 2 6 6 1 1 1 2 4 6 1 6 4 4 3 6 1 6 1 6 2 1 3 2 2 2
[433] 2 1 5 2 5 4 2 1 6 2 6 2 2 1 4 3 1 2 6 5 5 4 3 4 2 2 2 1 4 3 2 2 5 4 2 1 2 5 4 4 4 2 6 1 3 1 5 2 5 1 1 3 4 1 2 1 4 1 6 6 1 2 3 4 3 6 5 2 5 6 1 1
[505] 2 3 1 5 2 2 1 6 6 1 1 2 6 5 2 4 1 1 2 2 6 6 3 6 6 4 1 1 2 2 3 1 4 4 2 2 6 5 2 1 4 6 2 3 6 5 5 6 3 6 2 6 2 2 6 4 5 6 6 6 4 5 3 1 2 2 4 2 3 4 4 3
[577] 2 5 4 1 6 4 5 2 3 1 2 6 6 2 2 4 5 6 2 4 4 5 1 3 1 4 4 5 3 1 1 6 6 6 5 4 5 5 3 6 4 4 5 6 3 1
Within cluster sum of squares by cluster:
[1] 530.2404 1007.9397 443.6147 818.0674 1044.9111 792.3165
(between_SS / total_SS = 90.5 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss" "size" "iter" "ifault"
>>>>>>> Stashed changes
# visualize clustering
library(factoextra)
fviz_cluster(kmeans.result, data = datasetClustering)
<<<<<<< Updated upstream
=======
#average silhouette for cluster k=6
library(cluster)
avg_sil <- silhouette(kmeans.result$cluster,dist(datasetClustering))
fviz_silhouette(avg_sil)
<<<<<<< Updated upstream
=======
#Within-cluster sum of squares wss
wss <- kmeans.result$tot.withinss
print(wss)
<<<<<<< Updated upstream
=======
[1] 4637.09
>>>>>>> Stashed changes
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
#BCubed
kmeans_cluster <- c(kmeans.result$cluster)
ground_truth <- c(classLabel)
data <- data.frame(cluster = kmeans_cluster, label = ground_truth)
# Function to calculate BCubed precision and recall
bcubed <- function(data) {
n <- nrow(data)
total_precesion <- 0
total_recall <- 0
for (i in 1:n) {
cluster <- data$cluster[i]
label <- data$label[i]
# Count the number of items from the same category within the same cluster
intersection <- sum(data$label[data$cluster == cluster] == label)
# Count the total number of items in the same cluster
total_same_cluster <- sum(data$cluster == cluster)
# Count the total number of items with the same category
total_same_category <- sum(data$label == label)
# Calculate precision and recall for the current item and add them to the sums
total_precesion <- total_precesion + intersection /total_same_cluster
total_recall <- total_recall + intersection / total_same_category
}
# Calculate average precision and recall
precision <- total_precesion / n
recall <- total_recall / n
return(list(precision = precision, recall = recall))
}
# Calculate BCubed precision and recall
metrics <- bcubed(data)
# Extract precision and recall from the metrics
precision <- metrics$precision
recall <- metrics$recall
# Print the results
<<<<<<< Updated upstream
cat("BCubed Precision:", precision, "\n")
cat("BCubed Recall:", recall, "\n")
=======
cat("BCubed Precision:", precision, "\n")
BCubed Precision: 0.009646302
cat("BCubed Recall:", recall, "\n")
BCubed Recall: 1
>>>>>>> Stashed changes
| Measure | K=3 | K=4 | K=6 |
|---|---|---|---|
| Average Silhouette width | 0.55 | 0.54 | 0.52 |
| Total within-cluster sum of square | 10330.95 | 7024.453 | 4559.055 |
| BCubed precision | 0.00482315 | 0.00643087 | 0.00964630 |
| BCubed recall | 1.00 | 1.00 | 1.00 |
As the graph of K=6 illustrates , there is a noticeable overlapping between the clusters that effect the cluster performance duo to the similarity between clusters and wide distance in the cluster itself as the high value of wss indicate (90.5%) ,the recall value is 1 wich means that all items that must belong to their specified cluster has been correctly identified , the value of precision 0.00964630 is low which can be duo to the presence of outliers, the value of average silhouette width is 0.52 which is lower than the other K values.
Overall, the plots suggest that dividing the data into 3 clusters rather than 4 or 6 clusters seems appropriate because of the highest average Silhouette score. This implies that the 3-cluster solution has better cohesion and separation among the clusters: each data point is, on average, closer to its own cluster center and farther from other cluster centers compared to the 4 and 6 cluster solutions. The high average Silhouette score indicates that the 3-cluster model provides a more meaningful and well-defined cluster structure, where each cluster is distinct and contains members that are more similar to each other than to members of other clusters. This can lead to more actionable insights and reliable interpretations of the data’s underlying patterns.
As the graph of K=6 illustrated , there is a noticeable overlapping between the clusters that effect the cluster performance duo to the similarity between clusters and wide distance in the cluster itself as the high value of wss indicate (90.5%) ,the recall value is acceptable which is 0.22 and it is the lowest value between the other clusters, the value of precision 0.02 is low which can be duo to the presence of outliers, the value of average silhouette width is 0.52 which is bad for the clustering process it. Overall, the plot suggests that dividing the data into 3 clusters seems appropriate because the average Silhouette score .